NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "mysql_identifier_quote_character" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "mysql_identifier_quote_character" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/includes/database/mysql/database.inc [line] => 397 [function] => variable_get [args] => Array ( [0] => mysql_identifier_quote_character [1] => ` ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 329 [function] => setPrefix [class] => DatabaseConnection_mysql [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => common_fe [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => Array ( [default] => ) ) ) [3] => Array ( [file] => /apps/hitra7/drupal7/includes/database/mysql/database.inc [line] => 349 [function] => __construct [class] => DatabaseConnection [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => common_fe [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => mysql:host=localhost;port=3306;charset=utf8;dbname=common_fe [1] => root [2] => asdf [3] => Array ( [1000] => 1 [20] => 1 [17] => 1 [1013] => ) ) ) [4] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 1796 [function] => __construct [class] => DatabaseConnection_mysql [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => common_fe [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => Array ( [driver] => mysql [database] => common_fe [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) [pdo] => Array ( [1000] => 1 [20] => 1 [17] => 1 [1013] => ) ) ) ) [5] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 1582 [function] => openConnection [class] => Database [type] => :: [args] => Array ( [0] => common_fe [1] => default ) ) [6] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 2467 [function] => getConnection [class] => Database [type] => :: [args] => Array ( [0] => default ) ) [7] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 31 [function] => db_query [args] => Array ( [0] => SELECT * FROM fe_regions_translation frt LEFT JOIN fe_regions fr ON frt.region_id = fr.region_id WHERE frt.url_path_mapper = :region_path_mapper AND frt.language = :language AND fr.sales_area IN (:sales_areas) AND frt.region_publish_status = 1 [1] => Array ( [:region_path_mapper] => quito [:language] => es [:sales_areas] => Array ( [0] => ec_ecuador ) ) ) ) [8] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 175 [function] => np_db_query [args] => Array ( [0] => common_fe [1] => db_query [2] => SELECT * FROM fe_regions_translation frt LEFT JOIN fe_regions fr ON frt.region_id = fr.region_id WHERE frt.url_path_mapper = :region_path_mapper AND frt.language = :language AND fr.sales_area IN (:sales_areas) AND frt.region_publish_status = 1 [3] => Array ( [:region_path_mapper] => quito [:language] => es [:sales_areas] => Array ( [0] => ec_ecuador ) ) ) ) [9] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 58 [function] => find_region_by_url_path_mapper [args] => Array ( [0] => quito [1] => es ) ) [10] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [11] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [12] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [13] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [14] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "mysql_identifier_quote_character" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "mysql_identifier_quote_character" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/includes/database/mysql/database.inc [line] => 397 [function] => variable_get [args] => Array ( [0] => mysql_identifier_quote_character [1] => ` ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 329 [function] => setPrefix [class] => DatabaseConnection_mysql [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => hitrahr [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => Array ( [default] => ) ) ) [3] => Array ( [file] => /apps/hitra7/drupal7/includes/database/mysql/database.inc [line] => 349 [function] => __construct [class] => DatabaseConnection [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => hitrahr [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => mysql:host=localhost;port=3306;charset=utf8;dbname=hitrahr [1] => root [2] => asdf [3] => Array ( [1000] => 1 [20] => 1 [17] => 1 [1013] => ) ) ) [4] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 1796 [function] => __construct [class] => DatabaseConnection_mysql [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => hitrahr [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => 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[function] => nobleprog_frontend_get_category_description [args] => Array ( [0] => stdClass Object ( [tid] => 4751 [url_alias] => cursos-hugging-face [name] => Hugging Face [description] =>

En línea o en el sitio, los cursos de capacitación en vivo de Hugging Face dirigidos por un instructor demuestran a través de la práctica interactiva cómo utilizar eficazmente Hugging Face para tareas de procesamiento de lenguaje natural (NLP). El entrenamiento de Hugging Face está disponible como "entrenamiento en vivo en línea" o "entrenamiento en vivo en el sitio". La capacitación en vivo en línea (también conocida como "capacitación remota en vivo") se lleva a cabo a través de un escritorio remoto interactivo La capacitación en vivo in situ se puede llevar a cabo localmente en las instalaciones del cliente en o en los centros de capacitación corporativa de NobleProg en . NobleProg -- Su proveedor local de capacitación

[language] => [url_path_mapper] => hugging-face [english_name] => Hugging Face [original_description] => Online or onsite, instructor-led live Hugging Face training courses demonstrate through interactive hands-on practice how to effectively utilize Hugging Face for natural language processing (NLP) tasks. Hugging Face training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live training can be carried out locally on customer premises in or in NobleProg corporate training centers in . NobleProg -- Your Local Training Provider [consulting_options] => available ) [1] => Hugging Face [2] => Ecuador [3] => stdClass Object ( [language] => es [name] => Spanish [native] => Español [prefix] => es [default_language] => es [language_url] => [secondary_language] => [language_switcher_links] => Array ( [es] => /hugging-face/cursos/quito [en] => /en/hugging-face/training/quito ) [multi_lingual] => 1 ) ) ) [2] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [3] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [4] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [6] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "es" /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/np_outline/np_outline.module:66 Array ( [0] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/np_outline/np_outline.module [line] => 66 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "es" [2] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/np_outline/np_outline.module [3] => 66 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/np_outline/np_outline.module [line] => 25 [function] => np_outline_title [args] => Array ( [0] => huggingface [1] => ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/common/hrquery/frontend/denorm.logic.php [line] => 46 [function] => np_outline_get_value [args] => Array ( [0] => Array ( [en] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>

Audience

[overview] =>

Hugging Face is a powerful open-source library and platform for natural language processing (NLP). 

This instructor-led, live training (online or onsite) is aimed at data scientists, machine learning practitioners, and NLP researchers and enthusiasts who wish to effectively utilize Hugging Face for NLP tasks.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at data scientists, machine learning practitioners, and NLP researchers and enthusiasts who wish to effectively utilize Hugging Face for NLP tasks.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Setting up a working environment

Understanding the Hugging Face Transformers library and Transformer Models

Utilizing Hugging Face Transformers

Fine-Tuning a Pretrained Model

Sharing Models and Tokenizers

Exploring Hugging Face Datasets Library

Exploring Hugging Face Tokenizers Library

Carrying out Classic NLP Tasks

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

Troubleshooting and Debugging

Building and Sharing Your Model Demos

Summary and Next Steps

[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) ) [es] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] => [overview] =>

Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN).

Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL.

Al finalizar esta capacitación, los participantes podrán:

Formato del curso

Opciones de personalización del curso

[category_overview] =>

Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.

Al final de esta capacitación, los participantes podrán:

[outline] => [language] => es [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => 1 [field_course_outline] => 1 [field_prerequisits] => [field_overview_in_category] => ) ) ) ) ) [3] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/common/hrquery/frontend/denorm.logic.php [line] => 54 [function] => hrquery_get_outline_by_cc_langs [args] => Array ( [0] => huggingface [1] => Array ( [0] => en [1] => es ) ) ) [4] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/common/hrquery/frontend/denorm.logic.php [line] => 63 [function] => _hrquery_get_outline_by_course_code [args] => Array ( [0] => huggingface [1] => Array ( [0] => en [1] => es ) ) ) [5] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/common/hrquery/frontend/denorm.logic.php [line] => 74 [function] => hrquery_get_outline_by_course_code [args] => Array ( [0] => Array ( [0] => huggingface ) [1] => Array ( [0] => en [1] => es ) ) ) [6] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/common/hrquery/frontend/denorm.logic.php [line] => 118 [function] => hrquery_get_outlines_by_course_codes [args] => Array ( [0] => Array ( [0] => huggingface ) ) ) [7] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-functions.php [line] => 25 [function] => hrquery_denorm_get_outlines_with_plain_text_overview_by_course_codes [args] => Array ( [0] => Array ( [0] => huggingface ) ) ) [8] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 313 [function] => term_cat_page_render_course_in_category_v2 [args] => Array ( [0] => 4751 [1] => 1 ) ) [9] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 91 [function] => category_get_outlines_in_category [args] => Array ( [0] => Array ( [tid] => 4751 [url_alias] => cursos-hugging-face [name] => Hugging Face [description] =>

En línea o en el sitio, los cursos de capacitación en vivo de Hugging Face dirigidos por un instructor demuestran a través de la práctica interactiva cómo utilizar eficazmente Hugging Face para tareas de procesamiento de lenguaje natural (NLP). El entrenamiento de Hugging Face está disponible como "entrenamiento en vivo en línea" o "entrenamiento en vivo en el sitio". La capacitación en vivo en línea (también conocida como "capacitación remota en vivo") se lleva a cabo a través de un escritorio remoto interactivo La capacitación en vivo in situ se puede llevar a cabo localmente en las instalaciones del cliente en o en los centros de capacitación corporativa de NobleProg en . NobleProg -- Su proveedor local de capacitación

[language] => [url_path_mapper] => hugging-face [english_name] => Hugging Face [original_description] => Online or onsite, instructor-led live Hugging Face training courses demonstrate through interactive hands-on practice how to effectively utilize Hugging Face for natural language processing (NLP) tasks. Hugging Face training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live training can be carried out locally on customer premises in or in NobleProg corporate training centers in . NobleProg -- Your Local Training Provider [consulting_options] => available ) ) ) [10] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [11] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [12] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [13] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [14] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_site_production_url" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_site_production_url" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7.module [line] => 131 [function] => variable_get [args] => Array ( [0] => nobleprog_site_production_url ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7.module [line] => 94 [function] => islc_get_current_site [args] => Array ( ) ) [3] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7_block.inc [line] => 34 [function] => islc_get_site_list [args] => Array ( ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 94 [function] => islc7_sites_links_array_v3 [args] => Array ( ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "devel_domain" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "devel_domain" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7.module [line] => 99 [function] => variable_get [args] => Array ( [0] => devel_domain [1] => ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7_block.inc [line] => 34 [function] => islc_get_site_list [args] => Array ( ) ) [3] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 94 [function] => islc7_sites_links_array_v3 [args] => Array ( ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [5] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [7] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [8] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_site_production_url" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_site_production_url" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7.module [line] => 131 [function] => variable_get [args] => Array ( [0] => nobleprog_site_production_url ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7_block.inc [line] => 44 [function] => islc_get_current_site [args] => Array ( ) ) [3] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 94 [function] => islc7_sites_links_array_v3 [args] => Array ( ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [5] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [7] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [8] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [2] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 3937 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => huggingface [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => huggingface ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [huggingface] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
[language] => en [duration] => 14 [status] => published [changed] => 1700037956 [source_title] => Hugging Face for Natural Language Processing (NLP) [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => huggingface ) ) [1] => Array ( [0] => ec_4967 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => huggingface [hr_nid] => 442391 [title] => Hugging Face for Natural Language Processing (NLP) [requirements] =>
  • A good knowledge of Python
  • Experience with deep learning
  • Familiarity with PyTorch or TensorFlow is beneficial but not required

Audience

  • Data scientists
  • Machine learning practitioners
  • NLP researchers and enthusiasts
  • Developers interested in implementing NLP solutions
[overview] => Hugging Face es una potente biblioteca y plataforma de código abierto para el procesamiento del lenguaje natural (PLN). Esta capacitación en vivo dirigida por un instructor (en el sitio o remota) está dirigida a científicos de datos, profesionales del aprendizaje automático e investigadores y entusiastas de la PNL que deseen utilizar Hugging Face de manera efectiva para tareas de PNL. Al finalizar esta capacitación, los participantes podrán:
  • Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico.
  • Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL.
  • Cree y comparta sus demostraciones de modelos de manera efectiva.
  • Agilice la optimización de sus modelos para producción.
  • Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
Formato del curso
  • Conferencia interactiva y discusión.
  • Muchos ejercicios y práctica.
  • Implementación práctica en un entorno de laboratorio en vivo.
Opciones de personalización del curso
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => Esta capacitación en vivo dirigida por un instructor en <loc> (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
[outline] =>

Introduction

  • Overview of NLP and its applications
  • Introduction to Hugging Face and its key features

Setting up a working environment

  • Installing and configuring Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

  • Exploring the Transformers library structure and functionalities
  • Overview of various Transformer models available in Hugging Face

Utilizing Hugging Face Transformers

  • Loading and using pretrained models
  • Applying Transformers for various NLP tasks

Fine-Tuning a Pretrained Model

  • Preparing a dataset for fine-tuning
  • Fine-tuning a Transformer model on a specific task

Sharing Models and Tokenizers

  • Exporting and sharing trained models
  • Utilizing tokenizers for text processing

Exploring Hugging Face Datasets Library

  • Overview of the Datasets library in Hugging Face
  • Accessing and utilizing pre-existing datasets

Exploring Hugging Face Tokenizers Library

  • Understanding tokenization techniques and their importance
  • Leveraging tokenizers from Hugging Face

Carrying out Classic NLP Tasks

  • Implementing common NLP tasks using Hugging Face
  • Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

  • Extending the use of Transformers beyond text-based tasks
  • Applying Transformers for speech and image-related tasks

Troubleshooting and Debugging

  • Common issues and challenges in working with Hugging Face
  • Techniques for troubleshooting and debugging

Building and Sharing Your Model Demos

  • Designing and creating interactive model demos
  • Sharing and showcasing your models effectively

Summary and Next Steps

  • Recap of key concepts and techniques learned
  • Guidance on further exploration and resources for continued learning
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En línea o en el sitio, los cursos de capacitación en vivo de Hugging Face dirigidos por un instructor demuestran a través de la práctica interactiva cómo utilizar eficazmente Hugging Face para tareas de procesamiento de lenguaje natural (NLP). El entrenamiento de Hugging Face está disponible como "entrenamiento en vivo en línea" o "entrenamiento en vivo en el sitio". La capacitación en vivo en línea (también conocida como "capacitación remota en vivo") se lleva a cabo a través de un escritorio remoto interactivo La capacitación en vivo in situ se puede llevar a cabo localmente en las instalaciones del cliente en o en los centros de capacitación corporativa de NobleProg en . NobleProg -- Su proveedor local de capacitación

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En línea o en el sitio, los cursos de capacitación en vivo de Hugging Face dirigidos por un instructor demuestran a través de la práctica interactiva cómo utilizar eficazmente Hugging Face para tareas de procesamiento de lenguaje natural (NLP). El entrenamiento de Hugging Face está disponible como "entrenamiento en vivo en línea" o "entrenamiento en vivo en el sitio". La capacitación en vivo en línea (también conocida como "capacitación remota en vivo") se lleva a cabo a través de un escritorio remoto interactivo La capacitación en vivo in situ se puede llevar a cabo localmente en las instalaciones del cliente en o en los centros de capacitación corporativa de NobleProg en . NobleProg -- Su proveedor local de capacitación

[language] => [url_path_mapper] => hugging-face [english_name] => Hugging Face [original_description] => Online or onsite, instructor-led live Hugging Face training courses demonstrate through interactive hands-on practice how to effectively utilize Hugging Face for natural language processing (NLP) tasks. Hugging Face training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live training can be carried out locally on customer premises in or in NobleProg corporate training centers in . NobleProg -- Your Local Training Provider [consulting_options] => available ) [2] => hitraec ) ) [3] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [4] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [5] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [6] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [7] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/hugging-face/cursos/quito Undefined property: stdClass::$machine_description /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/nobleprog_frontend/nobleprog_frontend_category_description.logic.php:50 Array ( [0] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/nobleprog_frontend/nobleprog_frontend_category_description.logic.php [line] => 50 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined property: stdClass::$machine_description [2] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/nobleprog_frontend/nobleprog_frontend_category_description.logic.php [3] => 50 ) ) [1] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 107 [function] => nobleprog_frontend_get_category_description [args] => Array ( [0] => stdClass Object ( [tid] => 4751 [url_alias] => cursos-hugging-face [name] => Hugging Face [description] =>

En línea o en el sitio, los cursos de capacitación en vivo de Hugging Face dirigidos por un instructor demuestran a través de la práctica interactiva cómo utilizar eficazmente Hugging Face para tareas de procesamiento de lenguaje natural (NLP). El entrenamiento de Hugging Face está disponible como "entrenamiento en vivo en línea" o "entrenamiento en vivo en el sitio". La capacitación en vivo en línea (también conocida como "capacitación remota en vivo") se lleva a cabo a través de un escritorio remoto interactivo La capacitación en vivo in situ se puede llevar a cabo localmente en las instalaciones del cliente en o en los centros de capacitación corporativa de NobleProg en . NobleProg -- Su proveedor local de capacitación

[language] => [url_path_mapper] => hugging-face [english_name] => Hugging Face [original_description] => Online or onsite, instructor-led live Hugging Face training courses demonstrate through interactive hands-on practice how to effectively utilize Hugging Face for natural language processing (NLP) tasks. Hugging Face training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live training can be carried out locally on customer premises in or in NobleProg corporate training centers in . NobleProg -- Your Local Training Provider [consulting_options] => available ) [1] => Hugging Face [2] => Quito [3] => stdClass Object ( [language] => es [name] => Spanish [native] => Español [prefix] => es [default_language] => es [language_url] => [secondary_language] => [language_switcher_links] => Array ( [es] => /hugging-face/cursos/quito [en] => /en/hugging-face/training/quito ) [multi_lingual] => 1 ) ) ) [2] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => hugging-face [1] => quito ) ) [3] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /hugging-face/cursos/quito ) ) [4] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [6] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) Cursos de Hugging Face en Quito

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Hugging Face for Natural Language Processing (NLP)

 14 horas
Esta capacitación en vivo dirigida por un instructor en Quito (en el sitio o remota) está dirigida a científicos de datos, profesionales de aprendizaje automático e investigadores y entusiastas de PNL que desean utilizar Hugging Face para tareas de PNL de manera efectiva.Al final de esta capacitación, los participantes podrán:
    Utilice un modelo de transformador Hugging Face y ajústelo en un conjunto de datos específico. Obtenga la capacidad de abordar de forma independiente los desafíos comunes de la PNL. Cree y comparta sus demostraciones de modelos de manera efectiva. Agilice la optimización de sus modelos para la producción. Emplee Hugging Face Transformers para resolver una amplia gama de problemas de aprendizaje automático.
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