Natural Language Processing in Action, Second Edition
eBook - ePub

Natural Language Processing in Action, Second Edition

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Natural Language Processing in Action, Second Edition

About this book

Develop your NLP skills from scratch, with an open source toolbox of Python packages, Transformers, Hugging Face, vector databases, and your own Large Language Models.

Natural Language Processing in Action, Second Edition has helped thousands of data scientists build machines that understand human language. In this new and revised edition, you’ll discover state-of-the art Natural Language Processing (NLP) models like BERT and HuggingFace transformers, popular open-source frameworks for chatbots, and more. You’ll create NLP tools that can detect fake news, filter spam, deliver exceptional search results and even build truthfulness and reasoning into Large Language Models (LLMs).

In Natural Language Processing in Action, Second Edition you will learn how to:

• Process, analyze, understand, and generate natural language text
• Build production-quality NLP pipelines with spaCy
• Build neural networks for NLP using Pytorch
• BERT and GPT transformers for English composition, writing code, and even organizing your thoughts
• Create chatbots and other conversational AI agents

In this new and revised edition, you’ll discover state-of-the art NLP models like BERT and HuggingFace transformers, popular open-source frameworks for chatbots, and more. Plus, you’ll discover vital skills and techniques for optimizing LLMs including conversational design, and automating the “trial and error” of LLM interactions for effective and accurate results.

About the technology

From nearly human chatbots to ultra-personalized business reports to AI-generated email, news stories, and novels, natural language processing (NLP) has never been more powerful! Groundbreaking advances in deep learning have made high-quality open source models and powerful NLP tools like spaCy and PyTorch widely available and ready for production applications. This book is your entrance ticket—and backstage pass—into the next generation of natural language processing.

About the book

Natural Language Processing in Action, Second Edition introduces the foundational technologies and state-of-the-art tools you’ll need to write and publish NLP applications. You learn how to create custom models for search, translation, writing assistants, and more, without relying on big commercial foundation models. This fully updated second edition includes coverage of BERT, Hugging Face transformers, fine-tuning large language models, and more.

What's inside

• NLP pipelines with spaCy
• Neural networks with PyTorch
• BERT and GPT transformers
• Conversational design for chatbots

About the reader

For intermediate Python programmers familiar with deep learning basics.

About the author

Hobson Lane is a data scientist and machine learning engineer with over twenty years of experience building autonomous systems and NLP pipelines. Maria Dyshel is a social entrepreneur and artificial intelligence expert, and the CEO and cofounder of Tangible AI.

Cole Howard and Hannes Max Hapke were co-authors of the first edition.

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Yes, you can access Natural Language Processing in Action, Second Edition by Hobson Lane,Maria Dyshel in PDF and/or ePUB format, as well as other popular books in Computer Science & Natural Language Processing. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Praise for the First Edition
  2. Natural Language Processing in Action, Second Edition
  3. copyright
  4. contents
  5. preface
  6. acknowledgments
  7. about this book
  8. about the authors
  9. about the contributors
  10. about the cover illustration
  11. Part 1 Wordy machines: Vector models of natural language
  12. 1 Machines that read and write: A natural language processing overview
  13. 2 Tokens of thought: Natural language words
  14. 3 Math with words: Term frequency–inverse document frequency vectors
  15. 4 Finding meaning in word counts: Semantic analysis
  16. Part 2 Deeper learning: Neural networks
  17. 5 Word brain: Neural networks
  18. 6 Reasoning with word embeddings
  19. 7 Finding kernels of knowledge in text with CNNs
  20. 8 Reduce, reuse, and recycle your words: RNNs and LSTMs
  21. Part 3 Getting real: Real-world NLP applications
  22. 9 Stackable deep learning: Transformers
  23. 10 Large language models in the real world
  24. 11 Information extraction and knowledge graphs
  25. 12 Getting chatty with dialog engines
  26. appendix A Your NLP tools
  27. appendix B Playful Python and regular expressions
  28. appendix C Vectors and linear algebra
  29. appendix D Machine learning tools and techniques
  30. appendix E Deploying NLU containerized microservices
  31. appendix F Glossary
  32. index