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Keras Deep Learning Cookbook

You're reading from   Keras Deep Learning Cookbook Over 30 recipes for implementing deep neural networks in Python

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781788621755
Length 252 pages
Edition 1st Edition
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Authors (3):
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Sujit Pal Sujit Pal
Author Profile Icon Sujit Pal
Sujit Pal
Manpreet Singh Ghotra Manpreet Singh Ghotra
Author Profile Icon Manpreet Singh Ghotra
Manpreet Singh Ghotra
Rajdeep Dua Rajdeep Dua
Author Profile Icon Rajdeep Dua
Rajdeep Dua
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Toc

Table of Contents (12) Chapters Close

Preface 1. Keras Installation 2. Working with Keras Datasets and Models FREE CHAPTER 3. Data Preprocessing, Optimization, and Visualization 4. Classification Using Different Keras Layers 5. Implementing Convolutional Neural Networks 6. Generative Adversarial Networks 7. Recurrent Neural Networks 8. Natural Language Processing Using Keras Models 9. Text Summarization Using Keras Models 10. Reinforcement Learning 11. Other Books You May Enjoy

Introduction

In this chapter, we will learn various recipes on how to create recurrent neural networks (RNNs) using Keras. First, we will understand the need for RNN. We will start with the simple RNNs followed by long short-term memory (LSTM) RNNs (these networks remember the state over a long period of time because of special gates in the cell).

The need for RNNs

Traditional neural networks cannot remember their past interactions, and that is a significant shortcoming. RNNs address this issue. They are networks with loops in them, allowing information to persist. RNNs have loops. In the next diagram, a chunk of the neural network, A, looks at some input, xt, and outputs a value, ht. A loop in the...

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