Natural Language Processing in Action: Understanding, analyzing, and generating text with Python
mar. de 2019 · Simon and Schuster
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Summary
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.
What's inside
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
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.
What's inside
- 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
- PART 1 - WORDY MACHINES
- Packets of thought (NLP overview)
- Build your vocabulary (word tokenization)
- Math with words (TF-IDF vectors)
- Finding meaning in word counts (semantic analysis) PART 2 - DEEPER LEARNING (NEURAL NETWORKS)
- 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 PART 3 - GETTING REAL (REAL-WORLD NLP CHALLENGES)
- Information extraction (named entity extraction and question answering)
- Getting chatty (dialog engines)
- Scaling up (optimization, parallelization, and batch processing)
Sobre o autor
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.
Hobson Lane is a data scientist and machine learning engineer. He has over twenty years experience building autonomous systems and NLP pipelines for both large corporations and startups. Currently, Hobson is an instructor at UCSD Extension and Springboard, and the CTO and cofounder of Tangible AI and ProAI.org.
Cole Howard is a carpenter and writer turned Deep Learning expert.
Hobson Lane is a data scientist and machine learning engineer. He has over twenty years experience building autonomous systems and NLP pipelines for both large corporations and startups. Currently, Hobson is an instructor at UCSD Extension and Springboard, and the CTO and cofounder of Tangible AI and ProAI.org.
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