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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

Setting up Deeplearning4j on Hadoop YARN

Deeplearning4j primarily works on networks having multiple layers. To get started working with Deeplearning4j, one needs to get accustomed with the prerequisites, and how to install all the dependent software. Most of the documentation can be easily found on the official website of Deeplearning4j at https://deeplearning4j.org/ [88].

In this section of the chapter, we will help you to get familiar with the code of Deeplearning4j. Initially, we will show the implementation of a simple operation of a multilayer neural network with Deeplearning4j. The later part of the section will discuss distributed deep learning with Deeplearning4j library. Deeplearning4j trains distributed deep neural network on multiple distributed GPUs using Apache Spark. The later part of this section will also introduce the setup of Apache Spark for Deeplearning4j.

Getting familiar with Deeplearning4j

This part will mainly introduce the 'Hello World' programs of deep learning with...

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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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