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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Sparse autoencoders

Distributed sparse representation is one of the primary keys to learn useful features in deep learning algorithms. Not only is it a coherent mode of data representation, but it also helps to capture the generation process of most of the real world dataset. In this section, we will explain how autoencoders encourage sparsity of data. We will start with introducing sparse coding. A code is termed as sparse when an input provokes the activation of a relatively small number of nodes of a neural network, which combine to represent it in a sparse way. In deep learning technology, a similar constraint is used to generate the sparse code models to implement regular autoencoders, which are trained with sparsity constants called sparse autoencoders.

Sparse coding

Sparse coding is a type of unsupervised method to learn sets of overcomplete bases in order to represent the data in a coherent and efficient way. The primary goal of sparse coding is to determine a set of vectors (n) vi...