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R Deep Learning Cookbook

You're reading from  R Deep Learning Cookbook

Product type Book
Published in Aug 2017
Publisher Packt
ISBN-13 9781787121089
Pages 288 pages
Edition 1st Edition
Languages
Authors (2):
PKS Prakash PKS Prakash
Profile icon PKS Prakash
Achyutuni Sri Krishna Rao Achyutuni Sri Krishna Rao
Profile icon Achyutuni Sri Krishna Rao
View More author details
Toc

Table of Contents (17) Chapters close

Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started 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

Training a Restricted Boltzmann machine


Every training step of an RBM goes through two phases: the forward phase and the backward phase (or reconstruction phase). The reconstruction of visible units is fine tuned by making several iterations of the forward and backward phases.

Training a forward phase: In the forward phase, the input data is passed from the visible layer to the hidden layer and all the computation occurs within the nodes of the hidden layer. The computation is essentially to take a stochastic decision of each connection from the visible to the hidden layer. In the hidden layer, the input data (X) is multiplied by the weight matrix (W) and added to a hidden bias vector (hb).

The resultant vector of a size equal to the number of hidden nodes is then passed through a sigmoid function to determine each hidden node's output (or activation state). In our case, each input digit will produce a tensor vector of 900 probabilities, and as we have 55,000 input digits, we will have an...

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