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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 (9) Chapters

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

Deep learning for massive amounts of data

In this Exa-Byte scale era, the data are increasing at an exponential rate. This growth of data are analyzed by many organizations and researchers in various ways, and also for so many different purposes. According to the survey of International Data Corporation (IDC), the Internet is processing approximately 2 Petabytes of data every day [51]. In 2006, the size of digital data was around 0.18 ZB, whereas this volume has increased to 1.8 ZB in 2011. Up to 2015, it was expected to reach up to 10 ZB in size, and by 2020, its volume in the world will reach up to approximately 30 ZB to 35 ZB. The timeline of this data mountain is shown in Figure 2.1. These immense amounts of data in the digital world are formally termed as big data.

	"The world of Big Data is on fire"
	--The Economist, Sept 2011

Deep learning for massive amounts of data

Figure 2.1: Figure shows the increasing trend of data for a time span of around 20 years

Facebook has almost 21 PB in 200M objects...

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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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