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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

Deep Learning with R

This chapter will build a foundation for neural networks followed by deep learning foundation and trends. We will cover the following topics:

  • Starting with logistic regression
  • Introducing the dataset
  • Performing logistic regression using H2O
  • Performing logistic regression using TensorFlow
  • Visualizing TensorFlow graphs
  • Starting with multilayer perceptrons
  • Setting up a neural network using H2O
  • Tuning hyper-parameters using grid searches in H2O
  • Setting up a neural network using MXNet
  • Setting up a neural network using TensorFlow
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