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Deep Learning with Hadoop

You're reading from   Deep Learning with Hadoop Distributed Deep Learning with Large-Scale Data

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781787124769
Length 206 pages
Edition 1st Edition
Languages
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Author (1):
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Dipayan Dev Dipayan Dev
Author Profile Icon Dipayan Dev
Dipayan Dev
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Table of Contents (9) Chapters Close

Preface 1. Introduction to Deep Learning FREE CHAPTER 2. Distributed Deep Learning for Large-Scale Data 3. Convolutional Neural Network 4. Recurrent Neural Network 5. Restricted Boltzmann Machines 6. Autoencoders 7. Miscellaneous Deep Learning Operations using Hadoop 1. References

Chapter 5.  Restricted Boltzmann Machines

 

"What I cannot create, I do not understand."

 
 --Richard Feynman

So far in this book, we have only discussed the discriminative models. The use of these in deep learning is to model the dependencies of an unobserved variable y on an observed variable x. Mathematically, it is formulated as P(y|x). In this chapter, we will discuss deep generative models to be used in deep learning.

Generative models are models, which when given some hidden parameters, can randomly generate some observable data values out of them. The model works on a joint probability distribution over label sequences and observation.

The generative models are used in machine and deep learning either as an intermediate step to generate a conditional probability density function or modeling observations directly from a probability density function.

Restricted Boltzmann machines (RBMs) are a popular generative model that will be discussed in this chapter...

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