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R Deep Learning Cookbook

You're reading from   R Deep Learning Cookbook Solve complex neural net problems with TensorFlow, H2O and MXNet

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787121089
Length 288 pages
Edition 1st Edition
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Authors (2):
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Achyutuni Sri Krishna Rao Achyutuni Sri Krishna Rao
Author Profile Icon Achyutuni Sri Krishna Rao
Achyutuni Sri Krishna Rao
PKS Prakash PKS Prakash
Author Profile Icon PKS Prakash
PKS Prakash
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Toc

Table of Contents (11) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Deep Learning with R 3. Convolution Neural Network 4. Data Representation Using Autoencoders 5. Generative Models in Deep Learning 6. Recurrent Neural Networks 7. Reinforcement Learning 8. Application of Deep Learning in Text Mining 9. Application of Deep Learning to Signal processing 10. Transfer Learning

Data Representation Using Autoencoders

This chapter will introduce unsupervised applications of deep learning using autoencoders. In this chapter, we will cover the following topics:

  • Setting up autoencoders
  • Data normalization
  • Setting up a regularized autoencoder
  • Fine-tuning the parameters of the autoencoder
  • Setting up stacked autoencoders
  • Setting up denoising autoencoders
  • Building and comparing stochastic encoders and decoders
  • Learning manifolds from autoencoders
  • Evaluating the sparse decomposition
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