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Natural Language Processing with TensorFlow
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Natural Language Processing with TensorFlow
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Overview of this book
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.
Table of Contents (14 chapters)
Preface
Free Chapter
1. Introduction to Natural Language Processing
2. Understanding TensorFlow
3. Word2vec – Learning Word Embeddings
4. Advanced Word2vec
5. Sentence Classification with Convolutional Neural Networks
6. Recurrent Neural Networks
7. Long Short-Term Memory Networks
8. Applications of LSTM – Generating Text
9. Applications of LSTM – Image Caption Generation
10. Sequence-to-Sequence Learning – Neural Machine Translation
11. Current Trends and the Future of Natural Language Processing
A. Mathematical Foundations and Advanced TensorFlow
Index
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