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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Deep autoencoders

So far, we have talked only about single-layer encoders and single-layer decoders for a simple autoencoder. However, a deep autoencoder with more than one encoder and decoder brings more advantages.

Feed-forward networks perform better when they are deep. Autoencoders are basically feed-forward networks; hence, the advantages of a basic feed-forward network can also be applied to autoencoders. The encoders and decoders are autoencoders, which also work like a feed-forward network. Hence, we can deploy the advantages of the depth of a feed-forward network in these components also.

In this context, we can also talk about the universal approximator theorem, which ensures that a feed-forward neural network with at least one hidden layer, and with enough hidden units, can produce an approximation of any arbitrary function to any degree of accuracy. Following this concept, a deep autoencoder having at least one hidden layer, and containing sufficient hidden units, can approximate...