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Natural Language Processing in Action de Hobson Lane

Natural Language Processing in Action

Autor Hobson Lane, Maria Dyshel
en Limba Engleză Paperback – 14 apr 2025
Develop your NLP skills from scratch! This revised bestseller now includes coverage of the latest Python packages, Transformers, the HuggingFace packages, and chatbot frameworks.Natural Language Processing in Action has helped thousands of data scientists build machines that understand human language. In this new and revised edition, youGÇÖll discover state-of-the art NLP models like BERT and HuggingFace transformers, popular open-source frameworks for chatbots, and more. As you go, youGÇÖll create projects that can detect fake news, filter spam, and even answer your questions, all built with Python and its ecosystem of data tools.
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.

In it, youGÇÖll learn to create fun and useful NLP applications such as semantic search engines that are even better than Google, chatbots that can help you write a book, and a multilingual translation program. Soon, youGÇÖll be ready to start tackling real-world problems with NLP.
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Specificații

ISBN-13: 9781617299445
ISBN-10: 1617299448
Pagini: 550
Dimensiuni: 187 x 235 x 35 mm
Greutate: 0.66 kg
Ediția:2 ed
Editura: Manning Publications

Cuprins

table of contents
PART 1: WORDY MACHINES (VECTOR MODELS OF NATURAL LANGUAGE)
READ IN LIVEBOOK1MACHINES THAT READ AND WRITE (NLP OVERVIEW)
READ IN LIVEBOOK2TOKENS OF THOUGHT (NATURAL LANGUAGE WORDS)
READ IN LIVEBOOK3MATH WITH WORDS (TF-IDF VECTORS)
READ IN LIVEBOOK4FINDING MEANING IN WORD COUNTS (SEMANTIC ANALYSIS)
PART 2: DEEPER LEARNING (NEURAL NETWORKS)
5 BABY STEPS WITH NEURAL NETWORKS (PERCEPTRONS AND BACKPROPAGATION)
6 REASONING WITH WORD VECTORS (WORD2VEC)
7 GETTING WORDS IN ORDER WITH CONVOLUTIONAL NEURAL NETWORKS (CNNS)
8 LOOPY (RECURRENT) NEURAL NETWORKS (RNNS)
9 IMPROVING RETENTION WITH LONG SHORT-TERM MEMORY NETWORKS (LSTMS)
10 SEQUENCE TO SEQUENCE MODELS AND ATTENTION (GENERATIVE MODELS)
PART 3: GETTING REAL (REAL WORLD NLP CHALLENGES)
11 INFORMATION EXTRACTION (NAMED ENTITY EXTRACTION AND QUESTION ANSWERING)
12 GETTING CHATTY (DIALOG ENGINES)
13 SCALING UP (OPTIMIZATION, PARALLELIZATION AND BATCH POCESSING)
APPENDICES
APPENDIX A: YOUR NLP TOOLS
APPENDIX B: PLAYFUL PYTHON AND REGULAR EXPRESSIONS
APPENDIX C: VECTORS AND MATRICES (BASIC LINEAR ALGEBRA)
APPENDIX D: MACHINE LEARNING
APPENDIX E: AWS GPU
APPENDIX F: LOCALITY SENSITIVE HASHING