Natural Language Processing in Action, Second Edition (MEAP V12)

✍ Scribed by Hobson Lane and Maria Dyshel

• Publisher: Manning Publications Co.
• Year: 2024
• Tongue: English
• Leaves: 942
• Edition: 2
• Category: Library

✦ Synopsis

Develop your NLP skills from scratch! This revised bestseller now includes coverage of the latest Python packages, Transformers, the HuggingFace packages, and chatbot frameworks.

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

Natural Language Processing in Action 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 NLP models like BERT and HuggingFace transformers, popular open-source frameworks for chatbots, and more. As you go, you’ll create projects that can detect fake news, filter spam, and even answer your questions, all built with Python and its ecosystem of data tools.

about the technology
Deep learning has been a giant leap forward for NLP applications. Developers can now build chatbots and other natural language tools that can imitate real people, diagnose illness, automatically summarize documents, and more. Accessible open source tools such as spaCy and PyTorch make production-level NLP easier and more impactful than ever before.

about the book
Natural Language Processing in Action, Second Edition is your guide to building software that can read and interpret human language. This new edition is updated to include the latest Python packages and comes with full coverage of cutting-edge models like BERT, GPT-J and HuggingFace transformers.

✦ Table of Contents

Welcome
1_Machines_that_read_and_write_(NLP_overview)
2_Tokens_of_thought_(natural_language_words)
3_Math_with_words_(TF-IDF_vectors)
4_Finding_meaning_in_word_counts_(semantic_analysis)
5_Word_brain_(neural_networks)
6_Reasoning_with_word_embeddings_(word_vectors)
7_Finding_Kernels_of_Knowledge_in_Text_with_Convolutional_Neural_Networks_(CNNs)
8_Reduce,Reuse,_Recycle_Your_Words(RNNs_and_LSTMs)
9_Stackable_deep_learning_(Transformers)
10_Large_Language_Models_in_the_real_world
11_Information_extraction_and_knowledge_graphs_(grounding)
12_Getting_Chatty_with_dialog_engines
Appendix_A._Your_NLP_tools
Appendix_B._Playful_Python_and_regular_expressions
Appendix_C._Vectors_and_Linear_Algebra
Appendix_D._Machine_learning_tools_and_techniques
Appendix_E._Deploy_NLU_containerized_microservices
Appendix_F._Glossary