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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 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

Introduction to Keras

Here we will provide a brief introduction to Keras, which is a sublibrary of TensorFlow that provides more high-level functions for implementing deep learning algorithms. Keras uses basic TensorFlow operations, underneath; however, it exposes a higher level, beginner-friendly API for users. To see how to use Keras, we will look at a quick example. We will outline how one might create a CNN using Keras. Full exercise can be found at keras_cnn.ipynb located in the appendix folder.

We will first determine what type of a model we will be defining. Keras has two different APIs: sequential and functional. The sequential API is simpler and allows designing a model, layer by layer. However, the sequential API has limited flexibility in designing the organization and connections between layers of the network. On the other hand, the functional API has much more flexibility and allows the user to design the specific details of the neural network. For demonstration purposes, we...

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