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Deep Learning for Natural Language Processing : A Gentle Introduction by Mihai Surdeanu and Marco Antonio Valenzuela-Escárcega (2024, Trade Paperback)

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About this product

Product Identifiers

PublisherCambridge University Press

ISBN-101009012657

ISBN-139781009012652

eBay Product ID (ePID)7065350899

Product Key Features

Number of Pages342 Pages

LanguageEnglish

Publication NameDeep Learning for Natural Language Processing : a Gentle Introduction

SubjectGeneral, Linguistics / General

Publication Year2024

TypeTextbook

Subject AreaMathematics, Language Arts & Disciplines

AuthorMihai Surdeanu, Marco Antonio Valenzuela-Escárcega

FormatTrade Paperback

Dimensions

Item Height0.7 in

Item Length9 in

Item Width5.9 in

Additional Product Features

Reviews'A wonderful introduction to natural language processing, emphasizing the machine learning fundamentals. The authors perfectly interleave theory with chapters giving practical implementations using PyTorch, and make it all seem easy, with a warm tone and clear and well-structured explanations. This book is a delight!' Dan Jurafsky, Professor of Linguistics and Computer Science, Stanford University

Dewey Edition23

IllustratedYes

Dewey Decimal006.35

Table Of ContentPreface; 1. Introduction; 2. The perception; 3. Logistic regression; 4. Implementing text classfication using perceptron and LR; 5. Feed forward neural networks; 6. Best practices in deep learning; 7. Implementing text classification with feed forward networks; 8. Distributional hypothesis and representation learning; 9. Implementing text classification using word embedding; 10. Recurrent neural networks; 11. Implementing POS tagging using RNNs; 12. Contexualized embeddings and transformer networks; 13. Using transformers with the hugging face library; 14. Encoder-decoder methods; 15. Implementing encoder-decoder methods; 16. Neural architecture for NLP applications; Appendix A: Overview of the python language and the key libraries; Appendix B: Character endcodings: ASCII and unicode.

SynopsisA 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., 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.

LC Classification NumberQA76.9.N38S8 2024