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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

Chapter 7. Miscellaneous Deep Learning Operations using Hadoop

	"In pioneer days they used oxen for heavy pulling, and when one ox couldn't budge a log, they didn't try to grow a larger ox. We shouldn't be trying for bigger computers, but for more systems of computers."
	--Grace Hopper

So far in this book, we discussed various deep neural network models and their concepts, applications, and implementation of the models in distributed environments. We have also explained why it is difficult for a centralized computer to store and process vast amounts of data and extract information using these models. Hadoop has been used to overcome the limitations caused by large-scale data.

As we have now reached the final chapter of this book, we will mainly discuss the design of the three most commonly used machine learning applications. We will explain the general concept of large-scale video processing, large-scale image processing, and natural language processing using the Hadoop framework.

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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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