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  • Deep Learning for Natural Language Processing: A Gentle Introduction by Mihai Su

    • Item No : 396264030746
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    • * Item Description

    • Deep Learning for Natural Language Processing

      by Mihai Surdeanu, Marco Antonio Valenzuela-Escárcega

      Estimated delivery 3-12 business days

      Format Paperback

      Condition Brand New

      Description A clear, accessible introduction to deep learning for natural language processing (NLP), this book is ideal for readers without a background in machine learning and NLP. It covers the necessary theoretical context using minimal jargon also covers practical aspects, using actual Python code for the neural architectures discussed.

      Publisher Description

      Deep Learning is becoming increasingly important in a technology-dominated world. However, the building of computational models that accurately represent linguistic structures is complex, as it involves an in-depth knowledge of neural networks, and the understanding of advanced mathematical concepts such as calculus and statistics. This book makes these complexities accessible to those from a humanities and social sciences background, by providing a clear introduction to deep learning for natural language processing. It covers both theoretical and practical aspects, and assumes minimal knowledge of machine learning, explaining the theory behind natural language in an easy-to-read way. It includes pseudo code for the simpler algorithms discussed, and actual Python code for the more complicated architectures, using modern deep learning libraries such as PyTorch and Hugging Face. Providing the necessary theoretical foundation and practical tools, this book will enable readers to immediately begin building real-world, practical natural language processing systems.

      Details

      • ISBN 1009012657
      • ISBN-13 9781009012652
      • Title Deep Learning for Natural Language Processing
      • Author Mihai Surdeanu, Marco Antonio Valenzuela-Escárcega
      • Format Paperback
      • Year 2024
      • Pages 344
      • Publisher Cambridge University Press
      GE_Item_ID:158316643;

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