Natural Language Processing with TensorFlow

English | 2018 | ISBN: 978-1788478311 | 472 Pages | PDF, EPUB | 32 MB

Natural Language Processing with TensorFlow: Teach language to machines using Python’s deep learning library
Write modern natural language processing applications using deep learning algorithms and TensorFlow
Natural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today’s data streams, and apply these tools to specific NLP tasks.
Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You’ll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply high-performance RNN models, like long short-term memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator.
After reading this book, you will gain an understanding of NLP and you’ll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks.
What You Will Learn

• Core concepts of NLP and various approaches to natural language processing
• How to solve NLP tasks by applying TensorFlow functions to create neural networks
• Strategies to process large amounts of data into word representations that can be used by deep learning applications
• Techniques for performing sentence classification and language generation using CNNs and RNNs
• About employing state-of-the art advanced RNNs, like long short-term memory, to solve complex text generation tasks
• How to write automatic translation programs and implement an actual neural machine translator from scratch
• The trends and innovations that are paving the future in NLP