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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 FREE CHAPTER 2. Working with Keras Datasets and Models 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

CIFAR-10 dataset

Load the CIFAR-10 small images classification dataset from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz. The CIFAR-10 dataset is made up of 60,000 32 x 32 color images in 10 classes, and there are 6000 images per class. The dataset consists of 50,000 training images and 10,000 test images.

The dataset has been divided into five training batches and one test batch, each with 10,000 images. The test batch contains 1,000 randomly selected images from each class. The training batches contain the rest of the images in a random order; some training batches may contain more images from one class than another. The training batches contain 5,000 images from each class, such as shown in the following image:

Reference: https://www.cs.toronto.edu/~kriz/cifar.html.

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