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Natural Language Processing in Action: Understanding, analyzing, and generating text with Python 1st Edition, Kindle Edition
Natural Language Processing in Action is your guide to creating machines that understand human language using the power of Python with its ecosystem of packages dedicated to NLP and AI.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Technology
Recent advances in deep learning empower applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, and automatically generated document summaries—all at a low cost. New techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before.
About the Book
Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you'll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions.
- Some sentences in this book were written by NLP! Can you guess which ones?
- Working with Keras, TensorFlow, gensim, and scikit-learn
- Rule-based and data-based NLP
- Scalable pipelines
About the Reader
This book requires a basic understanding of deep learning and intermediate Python skills.
About the Author
Hobson Lane, Cole Howard, and Hannes Max Hapke are experienced NLP engineers who use these techniques in production.
Table of Contents
- Packets of thought (NLP overview)
- Build your vocabulary (word tokenization)
- Math with words (TF-IDF vectors)
- Finding meaning in word counts (semantic analysis)
- Baby steps with neural networks (perceptrons and backpropagation)
- Reasoning with word vectors (Word2vec)
- Getting words in order with convolutional neural networks (CNNs)
- Loopy (recurrent) neural networks (RNNs)
- Improving retention with long short-term memory networks
- Sequence-to-sequence models and attention
- Information extraction (named entity extraction and question answering)
- Getting chatty (dialog engines)
- Scaling up (optimization, parallelization, and batch processing)
PART 1 - WORDY MACHINES
PART 2 - DEEPER LEARNING (NEURAL NETWORKS)
PART 3 - GETTING REAL (REAL-WORLD NLP CHALLENGES)
About the Author
Hobson Lane has more than 15 years of experience building autonomous systems that make important decisions on behalf of humans.
Hannes Hapke is an Electrical Engineer turned Data Scientist with experience in deep learning.
Cole Howard is a carpenter and writer turned Deep Learning expert.--This text refers to an alternate kindle_edition edition.
- ASIN : B097826WLF
- Publisher : Manning; 1st edition (16 March 2019)
- Language : English
- File size : 7450 KB
- Text-to-Speech : Enabled
- Screen Reader : Supported
- Enhanced typesetting : Enabled
- X-Ray : Not Enabled
- Word Wise : Not Enabled
- Print length : 544 pages
- Best Sellers Rank: 968,155 in Kindle Store (See Top 100 in Kindle Store)
- Customer Reviews:
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Top reviews from other countries
This is pretty gentle and easy introduction for the first few chapters, bag or words, tokenisation, dimension reduction and word vectors, before getting into the the details of recurrent networks and LSTM based encoders-decoder networks. I could follow the first two thirds of the book, some of the narrative was a little repetitive in the early chapters. There are some coding errata, dues to the pace of change in supporting libraries.
But it all gets a bit hairy around chapt10, with LSTM encoders-decoders. This is demanding stuff. The explanation is as good or better than other books (e.g. Chollets excellent deep learning book). But its still a little beyond my little brain. So it requires a deeper intellectual investment for the last b150 pages, and some serious investment in GPU and or cloud based Tensorflow processing capability.
Nothing is for free, and I fear that the advancement of deep NLP will remain within the big players like Google and Facebook if we are not careful.
This is my second Manning textbook (after Deep Learning with Python by Francois Chollet), and I definitely love this publishing house: textbooks written in conversational English (so it's great to read) with a reasonable depth and precision in explanations. Another bonus is that I absolutely love the covers artworks!
Reviewed in the United Kingdom on 8 December 2020