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About Hobson Lane
Hobson Lane is a machine learning engineer with a passion for teaching and writing. So it's no surprise that his first book teaches how to "compile" natural language into software that machines can execute. Hobson has been building control systems for 30 years, from offroad self-driving cars (TerraHawk) to spacefaring robots (NASA's AWIMR project and NGST's Formation Flying laboratory). Hobson's true passion is for robots that can communicate in natural language (English). His answer to the rise of antisocial chatbots is to build and train prosocial virtual assistants. He's on a mission to teach the world how to build chatbots that actually assist us rather than manipulate us. He built the first "visual interpreter for the blind" at Aira, and is now helping architect a cognitive assistant for medical providers at Manceps as well as a safety-monitoring smart camera for DeepCanopy. A how-to book on building cognitive assistants won't be far behind.
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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)