Deep learning for natural language processing:
Explore the most challenging issues of natural language processing, and learn how to solve them with cutting-edge deep learning! Deep learning has advanced natural language processing to exciting new levels and powerful new applications! For the first time, computer systems can achieve "human&q...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Shelter Island, NY
Manning Publications Co.
[2022]
|
| Ausgabe: | [First edition]. |
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781617295447/?ar |
| Zusammenfassung: | Explore the most challenging issues of natural language processing, and learn how to solve them with cutting-edge deep learning! Deep learning has advanced natural language processing to exciting new levels and powerful new applications! For the first time, computer systems can achieve "human" levels of summarizing, making connections, and other tasks that require comprehension and context. Deep Learning for Natural Language Processing reveals the groundbreaking techniques that make these innovations possible. Stephan Raaijmakers distills his extensive knowledge into useful best practices, real-world applications, and the inner workings of top NLP algorithms. Deep learning has transformed the field of natural language processing. Neural networks recognize not just words and phrases, but also patterns. Models infer meaning from context, and determine emotional tone. Powerful deep learning-based NLP models open up a goldmine of potential uses. Deep Learning for Natural Language Processing teaches you how to create advanced NLP applications using Python and the Keras deep learning library. You'll learn to use state-of the-art tools and techniques including BERT and XLNET, multitask learning, and deep memory-based NLP. Fascinating examples give you hands-on experience with a variety of real world NLP applications. Plus, the detailed code discussions show you exactly how to adapt each example to your own uses! |
| Beschreibung: | Includes bibliographical references and index |
| Umfang: | 1 Online-Ressource (296 Seiten) illustrations |
| ISBN: | 9781617295447 1617295442 9781638353997 1638353999 |
Internformat
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| discipline | Informatik |
| edition | [First edition]. |
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| id | ZDB-30-ORH-083657630 |
| illustrated | Illustrated |
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| isbn | 9781617295447 1617295442 9781638353997 1638353999 |
| language | English |
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| physical | 1 Online-Ressource (296 Seiten) illustrations |
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| publishDate | 2022 |
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| publisher | Manning Publications Co. |
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| spelling | Raaijmakers, Stephan VerfasserIn aut Deep learning for natural language processing Stephan Raaijmakers [First edition]. Shelter Island, NY Manning Publications Co. [2022] 1 Online-Ressource (296 Seiten) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index Explore the most challenging issues of natural language processing, and learn how to solve them with cutting-edge deep learning! Deep learning has advanced natural language processing to exciting new levels and powerful new applications! For the first time, computer systems can achieve "human" levels of summarizing, making connections, and other tasks that require comprehension and context. Deep Learning for Natural Language Processing reveals the groundbreaking techniques that make these innovations possible. Stephan Raaijmakers distills his extensive knowledge into useful best practices, real-world applications, and the inner workings of top NLP algorithms. Deep learning has transformed the field of natural language processing. Neural networks recognize not just words and phrases, but also patterns. Models infer meaning from context, and determine emotional tone. Powerful deep learning-based NLP models open up a goldmine of potential uses. Deep Learning for Natural Language Processing teaches you how to create advanced NLP applications using Python and the Keras deep learning library. You'll learn to use state-of the-art tools and techniques including BERT and XLNET, multitask learning, and deep memory-based NLP. Fascinating examples give you hands-on experience with a variety of real world NLP applications. Plus, the detailed code discussions show you exactly how to adapt each example to your own uses! Natural language processing (Computer science) Speech processing systems Machine learning Traitement automatique des langues naturelles Traitement automatique de la parole Apprentissage automatique |
| spellingShingle | Raaijmakers, Stephan Deep learning for natural language processing Natural language processing (Computer science) Speech processing systems Machine learning Traitement automatique des langues naturelles Traitement automatique de la parole Apprentissage automatique |
| title | Deep learning for natural language processing |
| title_auth | Deep learning for natural language processing |
| title_exact_search | Deep learning for natural language processing |
| title_full | Deep learning for natural language processing Stephan Raaijmakers |
| title_fullStr | Deep learning for natural language processing Stephan Raaijmakers |
| title_full_unstemmed | Deep learning for natural language processing Stephan Raaijmakers |
| title_short | Deep learning for natural language processing |
| title_sort | deep learning for natural language processing |
| topic | Natural language processing (Computer science) Speech processing systems Machine learning Traitement automatique des langues naturelles Traitement automatique de la parole Apprentissage automatique |
| topic_facet | Natural language processing (Computer science) Speech processing systems Machine learning Traitement automatique des langues naturelles Traitement automatique de la parole Apprentissage automatique |
| work_keys_str_mv | AT raaijmakersstephan deeplearningfornaturallanguageprocessing |