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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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Table of Contents (14) Chapters Close

Preface 1. Introduction to Natural Language Processing FREE CHAPTER 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

Chapter 2. Understanding TensorFlow

In this chapter, you will get an in-depth understanding of TensorFlow. This is an open source distributed numerical computation framework, and it will be the main platform on which we will be implementing all our exercises.

We will get started with TensorFlow by defining a simple calculation and trying to compute it using TensorFlow. After we successfully complete this, we will investigate how TensorFlow executes this computation. This will help us to understand how the framework creates a computational graph to compute the outputs and execute this graph through something known as a session. Then we will gain a hands-on experience of the TensorFlow architecture by relating how TensorFlow executes things, with the help of an analogy of how a restaurant might operate.

Having gained a good conceptual and technical understanding of how TensorFlow operates, we will look at some of the important computational operations that the framework offers. First...

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