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Deep Learning with R Cookbook

You're reading from   Deep Learning with R Cookbook Over 45 unique recipes to delve into neural network techniques using R 3.5.x

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781789805673
Length 328 pages
Edition 1st Edition
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Authors (3):
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Swarna Gupta Swarna Gupta
Author Profile Icon Swarna Gupta
Swarna Gupta
Rehan Ali Ansari Rehan Ali Ansari
Author Profile Icon Rehan Ali Ansari
Rehan Ali Ansari
Dipayan Sarkar Dipayan Sarkar
Author Profile Icon Dipayan Sarkar
Dipayan Sarkar
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Table of Contents (11) Chapters Close

Preface 1. Understanding Neural Networks and Deep Neural Networks 2. Working with Convolutional Neural Networks FREE CHAPTER 3. Recurrent Neural Networks in Action 4. Implementing Autoencoders with Keras 5. Deep Generative Models 6. Handling Big Data Using Large-Scale Deep Learning 7. Working with Text and Audio for NLP 8. Deep Learning for Computer Vision 9. Implementing Reinforcement Learning 10. Other Books You May Enjoy

Deep Generative Models

Deep generative neural networks are a popular form of unsupervised deep learning models. These models aim to learn the process that generates the data. Generative models not only learn to extract patterns from the data but also estimate the underlying probability distribution. These models are used to create synthetic data, which follows the same probability distribution as that of the given training dataset. This chapter will give you an idea of deep generative models and how they work.

In this chapter, we will cover the following recipes:

  • Generating images with GANs
  • Implementing DCGANs
  • Implementing variational autoencoders
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