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 6.  Autoencoders


"People worry that computers will get too smart and take over the world, but the real problem is that they're too stupid and they've already taken over the world."

 --Pedro Domingos

In the last chapter, we discussed a generative model called Restricted Boltzmann machine. In this chapter, we will introduce one more generative model called autoencoder. Autoencoder, a type of artificial neural network, is generally used for dimensionality reduction, feature learning, or extraction.

As we move on with this chapter, we will discuss the concept of autoencoder and its various forms in detail. We will also explain the terms regularized autoencoder and sparse autoencoder. The concept of sparse coding, and selection criteria of the sparse factor in a sparse autoencoder will be taken up. Later, we will talk about the deep learning model, deep autoencoder, and its implementation using Deeplearning4j. Denoising autoencoder is one more form of a traditional autoencoder, which...