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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

Setting up a Deep Belief Network


Deep belief networks are a type of Deep Neural Network (DNN), and are composed of multiple hidden layers (or latent variables). Here, the connections are present only between the layers and not within the nodes of each layer. The DBN can be trained both as an unsupervised and supervised model.

Note

The unsupervised model is used to reconstruct the input with noise removal and the supervised model (after pretraining) is used to perform classification. As there are no connections within the nodes in each layer, the DBNs can be considered as a set of unsupervised RBMs or autoencoders, where each hidden layer serves as a visible layer to its subsequent connected hidden layer.

This kind of stacked RBM enhances the performance of input reconstruction where CD is applied across all layers, starting from the actual input training layer and finishing at the last hidden (or latent) layer.

DBNs are a type of graphical model that train the stacked RBMs in a greedy manner...

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