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Natural Language Processing in Action, Second Edition
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
• 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.
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
• 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.
- GenresArtificial Intelligence
1333 pages, Kindle Edition
Published February 25, 2025
11 people are currently reading
23 people want to read
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Displaying 1 - 3 of 3 reviews
February 27, 2025
The second edition of Natural Language Processing in Action by Hobson Lane and Maria Dyshel is a valuable resource for anyone looking to master NLP. Building on the strengths of the first edition, this updated version introduces cutting-edge techniques while maintaining its hands-on, accessible approach. Whether you're a data scientist, AI researcher, or software engineer, this book is an invaluable guide to building practical NLP solutions using Python.
The second edition significantly expands on the original by incorporating recent advancements in NLP, particularly in deep learning and transformer models. Some major enhancements include:
• Expanded Coverage of Transformers – While the first edition introduced basic neural networks, this edition provides an in-depth look at state-of-the-art models like BERT, GPT, and T5, with practical implementations.
• More Hands-On Projects – This edition places even greater emphasis on real-world applications, offering projects such as semantic search engines, text classification, and multilingual translation.
• Integration with Modern Libraries – The first edition focused on foundational NLP with TensorFlow and scikit-learn, whereas the second edition updates the tech stack to include PyTorch and spaCy, ensuring compatibility with the latest industry trends.
• Conversational AI Development – Unlike the previous edition, this book dedicates a full section to chatbot development, covering dialogue management, response generation, and real-world deployment strategies.
In particular the following chapters stood out for me:
• Introduction to NLP Pipelines – This foundational chapter sets the stage by explaining tokenization, stemming, and lemmatization using spaCy, making it easier for newcomers to grasp core concepts.
• Building Neural Networks for NLP – Readers are guided through creating deep learning models with PyTorch, bridging the gap between traditional machine learning and modern AI techniques.
• Transformers and Transfer Learning – This is one of the most valuable chapters, as it walks through implementing cutting-edge transformer models, a topic that was only briefly touched upon in the first edition.
• Conversational AI and Chatbots – A brand-new addition, this chapter provides a roadmap for building interactive AI-driven assistants using open-source tools.
• Deploying NLP Models – Unlike the first edition, which focused mostly on model training, this chapter emphasizes real-world deployment strategies using cloud services and APIs.
What makes the second edition of Natural Language Processing in Action stand out is its ability to balance theory with practice. The authors' expertise shines through in their clear explanations, step-by-step code examples, and engaging writing style. With its inclusion of transformers, conversational AI, and modern NLP frameworks, it is undoubtedly one of the best resources for mastering natural language processing today. Whether you're just starting out or looking to advance your expertise, this book will be a valuable addition to your library.
The second edition significantly expands on the original by incorporating recent advancements in NLP, particularly in deep learning and transformer models. Some major enhancements include:
• Expanded Coverage of Transformers – While the first edition introduced basic neural networks, this edition provides an in-depth look at state-of-the-art models like BERT, GPT, and T5, with practical implementations.
• More Hands-On Projects – This edition places even greater emphasis on real-world applications, offering projects such as semantic search engines, text classification, and multilingual translation.
• Integration with Modern Libraries – The first edition focused on foundational NLP with TensorFlow and scikit-learn, whereas the second edition updates the tech stack to include PyTorch and spaCy, ensuring compatibility with the latest industry trends.
• Conversational AI Development – Unlike the previous edition, this book dedicates a full section to chatbot development, covering dialogue management, response generation, and real-world deployment strategies.
In particular the following chapters stood out for me:
• Introduction to NLP Pipelines – This foundational chapter sets the stage by explaining tokenization, stemming, and lemmatization using spaCy, making it easier for newcomers to grasp core concepts.
• Building Neural Networks for NLP – Readers are guided through creating deep learning models with PyTorch, bridging the gap between traditional machine learning and modern AI techniques.
• Transformers and Transfer Learning – This is one of the most valuable chapters, as it walks through implementing cutting-edge transformer models, a topic that was only briefly touched upon in the first edition.
• Conversational AI and Chatbots – A brand-new addition, this chapter provides a roadmap for building interactive AI-driven assistants using open-source tools.
• Deploying NLP Models – Unlike the first edition, which focused mostly on model training, this chapter emphasizes real-world deployment strategies using cloud services and APIs.
What makes the second edition of Natural Language Processing in Action stand out is its ability to balance theory with practice. The authors' expertise shines through in their clear explanations, step-by-step code examples, and engaging writing style. With its inclusion of transformers, conversational AI, and modern NLP frameworks, it is undoubtedly one of the best resources for mastering natural language processing today. Whether you're just starting out or looking to advance your expertise, this book will be a valuable addition to your library.
May 27, 2025
...Unlike a programming language, where each keyword has an unambiguous interpretation, natural languages are much more fuzzy. Think about the sentence, “The chicken is ready to eat.” This could mean that a live chicken is about to eat its breakfast—or that a cooked chicken is ready in the oven. This fuzziness of natural language leaves the interpretation of each word open to you and introduces interesting challenges in understanding and generating human language...
March 18, 2025
This book is great for both beginners and experienced ( NLP experts ) learners. It explains topics like preprocessing, TF-IDF, LDA, LSA, CNN, RNN, LSTM, and Attention in a clear and easy way. After each explanation, there are code examples to help us understand concepts better. Author uses real-life examples to show how the concepts work.
Yet to finish the book , will update the review(detailed review) once I finish the book.
Yet to finish the book , will update the review(detailed review) once I finish the book.
Displaying 1 - 3 of 3 reviews