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
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Dedication
Preface
References

Autoencoder


An autoencoder is a neural network with one hidden layer, which is trained to learn an identity function that attempts to reconstruct its input to its output. In other words, the autoencoder tries to copy the input data by projecting onto a lower dimensional subspace defined by the hidden nodes. The hidden layer, h, describes a code, which is used to represent the input data and its structure. This hidden layer is thus forced to learn the structure from its input training dataset so that it can copy the input at the output layer.

The network of an autoencoder can be split into two parts: encoder and decoder. The encoder is described by the function h=f (k), and a decoder that tries to reconstruct or copy is defined by r = g (h). The basic idea of autoencoder should be to copy only those aspects of the inputs which are prioritized, and not to create an exact replica of the input. They are designed in such a way so as to restrict the hidden layer to copy only approximately, and...