| Description |
1 online resource (296 pages) : illustrations |
| Bibliography |
Includes bibliographical references and index. |
| Summary |
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! |
| Subject |
Natural language processing (Computer science)
|
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Speech processing systems.
|
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Machine learning.
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Traitement automatique des langues naturelles. |
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Traitement automatique de la parole. |
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Apprentissage automatique. |
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Machine learning |
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Natural language processing (Computer science) |
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Speech processing systems |
| ISBN |
9781617295447 (electronic bk.) |
|
1617295442 (electronic bk.) |
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9781638353997 (electronic bk.) |
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1638353999 (electronic bk.) |
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