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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

Classification is an essential classical problem. The purpose of classification is to automatically classify a text corpus and images into one or more specified classes. In this chapter, we'll use the Keras library to train a classifier. Classification is a general task where machine learning is used, be it question answering, a support request, business question, cancer classification, and so on. This chapter describes how classification problems can be tackled with the open source neural network library. Compared to traditional algorithms, deep learning has several advantages for classification, including flexible models, lower amount of domain expertise being required, and easier ongoing learning.

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