Book Image

Deep Learning with Hadoop

By : Dipayan Dev
Book Image

Deep Learning with Hadoop

By: Dipayan Dev

Overview of this book

This book will teach you how to deploy large-scale dataset in deep neural networks with Hadoop for optimal performance. Starting with understanding what deep learning is, and what the various models associated with deep neural networks are, this book will then show you how to set up the Hadoop environment for deep learning. In this book, you will also learn how to overcome the challenges that you face while implementing distributed deep learning with large-scale unstructured datasets. The book will also show you how you can implement and parallelize the widely used deep learning models such as Deep Belief Networks, Convolutional Neural Networks, Recurrent Neural Networks, Restricted Boltzmann machines and autoencoder using the popular deep learning library Deeplearning4j. Get in-depth mathematical explanations and visual representations to help you understand the design and implementations of Recurrent Neural network and Denoising Autoencoders with Deeplearning4j. To give you a more practical perspective, the book will also teach you the implementation of large-scale video processing, image processing and natural language processing on Hadoop. By the end of this book, you will know how to deploy various deep neural networks in distributed systems using Hadoop.
Table of Contents (16 chapters)
Deep Learning with Hadoop
About the Author
About the Reviewers
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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. RBMs are basically probabilistic...