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Natural Language Processing with TensorFlow

You're reading from   Natural Language Processing with TensorFlow Teach language to machines using Python's deep learning library

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788478311
Length 472 pages
Edition 1st Edition
Languages
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Authors (2):
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Thushan Ganegedara Thushan Ganegedara
Author Profile Icon Thushan Ganegedara
Thushan Ganegedara
Motaz Saad Motaz Saad
Author Profile Icon Motaz Saad
Motaz Saad
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Toc

Table of Contents (14) Chapters Close

Preface 1. Introduction to Natural Language Processing 2. Understanding TensorFlow FREE CHAPTER 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

Introduction to the TensorFlow seq2seq library

We used the raw TensorFlow API for all our implementations in this book for better transparency of the actual functionality of the models and for a better learning experience. However, TensorFlow has various libraries that hide all the fine-grained details of the implementations. This allows users to implement sequence-to-sequence models like the Neural Machine Translation (NMT) model we saw in Chapter 10, Sequence-to-Sequence Learning – Neural Machine Translation with fewer lines of code and without worrying about more specific technical details about how they work. Knowledge about these libraries is important as they provide a much cleaner way of using these models in production code or researching beyond the existing methods. Therefore, we will go through a quick introduction of how to use the TensorFlow seq2seq library. This code is available as an exercise in the seq2seq_nmt.ipynb file.

Defining embeddings for the encoder and decoder...

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