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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

Distributed deep CNN

This section of the chapter will introduce some extremely aggressive deep CNN architecture, associated challenges for these networks, and the need of much larger distributed computing to overcome this. This section will explain how Hadoop and its YARN can provide a sufficient solution for this problem.

Most popular aggressive deep neural networks and their configurations

CNNs have shown stunning results in image recognition in recent years. However, unfortunately, they are extremely expensive to train. In the case of a sequential training process, the convolution operation takes around 95% of the total running time. With big datasets, even with low-scale distributed training, the training process takes many days to complete. The award winning CNN, AlexNet with ImageNet in 2012, took nearly an entire week to train on with two GTX 580 3 GB GPUs. The following table displays few of the most popular distributed deep CNNs with their configuration and corresponding time taken...

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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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