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Deep Learning with Hadoop

You're reading from Deep Learning with Hadoop Distributed Deep Learning with Large-Scale Data

Product type Paperback

Published in Feb 2017

Publisher Packt

ISBN-13 9781787124769

Length 206 pages

Edition 1st Edition

Languages

Java

Tools

Deeplearning4j

Concepts

Deep Learning

Author (1):

Dipayan Dev

View More author details

Table of Contents (16) Chapters

Deep Learning with Hadoop

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Dedication

Preface

1. Introduction to Deep Learning FREE CHAPTER

2. Distributed Deep Learning for Large-Scale Data

3. Convolutional Neural Network

4. Recurrent Neural Network

5. Restricted Boltzmann Machines

6. Autoencoders

7. Miscellaneous Deep Learning Operations using Hadoop

1. References

Summary

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.

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Authors (1)

Dipayan Dev

Dipayan Dev

Dipayan Dev has completed his M.Tech from National Institute of Technology, Silchar with a first class first and is currently working as a software professional in Bengaluru, India. He has extensive knowledge and experience in non-relational database technologies, having primarily worked with large-scale data over the last few years. His core expertise lies in Hadoop Framework. During his postgraduation, Dipayan had built an infinite scalable framework for Hadoop, called Dr. Hadoop, which got published in top-tier SCI-E indexed journal of Springer (http://link.springer.com/article/10.1631/FITEE.1500015). Dr. Hadoop has recently been cited by Goo Wikipedia in their Apache Hadoop article. Apart from that, he registers interest in a wide range of distributed system technologies, such as Redis, Apache Spark, Elasticsearch, Hive, Pig, Riak, and other NoSQL databases. Dipayan has also authored various research papers and book chapters, which are published by IEEE and top-tier Springer Journals. To know more about him, you can also visit his LinkedIn profile https://www.linkedin.com/in/dipayandev.

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