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
Customer Feedback

Distributed Deep Belief network

DBNs have so far achieved a lot in numerous applications such as speech and phone recognition [127], information retrieval [128], human motion modelling[129], and so on. However, the sequential implementation for both RBM and DBNs come with various limitations. With a large-scale dataset, the models show various shortcomings in their applications due to the long, time consuming computation involved, memory demanding nature of the algorithms, and so on. To work with Big data, RBMs and DBNs require distributed computing to provide scalable, coherent and efficient learning.

To make DBNs acquiescent to the large-scale dataset stored on a cluster of computers, DBNs should acquire a distributed learning approach with Hadoop and Map-Reduce. The paper in [130] has shown a key-value pair approach for each level of an RBM, where the pre-training is accomplished with layer-wise, in a distributed environment in Map-Reduce framework. The learning is performed on Hadoop...