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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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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

Setting up a Restricted Boltzmann machine for Bernoulli distribution input


In this section, let's set up a restricted Boltzmann machine for Bernoulli distributed input data, where each attribute has values ranging from 0 to 1 (equivalent to a probability distribution). The dataset (MNIST) used in this recipe has input data satisfying a Bernoulli distribution.

An RBM comprises of two layers: a visible layer and a hidden layer. The visible layer is an input layer of nodes equal to the number of input attributes. In our case, each image in the MNIST dataset is defined using 784 pixels (28 x 28 size). Hence, our visible layer will have 784 nodes.

On the other hand, the hidden layer is generally user-defined. The hidden layer has a set of binary activated nodes, with each node having a probability of linkage with all other visible nodes. In our case, the hidden layer will have 900 nodes. As an initial step, all the nodes in the visible layer are connected with all the nodes in the hidden layer...

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