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


The RBM is a generative model, which can randomly produce visible data values when some latent or hidden parameters are supplied to it. In this chapter, we have discussed the concept and mathematical model of the Boltzmann machine, which is an energy-based model. The chapter then discusses and gives a visual representation of the RBM. Further, this chapter discusses CRBM, which is a combination of Convolution and RBMs to extract the features of high dimensional images. We then moved toward popular DBNs that are nothing but a stacked implementation of RBMs. The chapter further discusses the approach to distribute the training of RBMs as well as DBNs in the Hadoop framework.

We conclude the chapter by providing code samples for both the models. The next chapter of the book will introduce one more generative model called autoencoder and its various forms such as de-noising autoencoder, deep autoencoder, and so on.