Deep Learning with Hadoop

Dipayan Dev

• Editore: Packt Publishing
• Anno: 2017
• ISBN:
  9781787124769
• Edizione: 1
• Argomento: Ciencia de la computación
• Categoria: Modelado y diseño de datos

Build, implement and scale distributed deep learning models for large-scale datasets

About This Book

• Get to grips with the deep learning concepts and set up Hadoop to put them to use
• Implement and parallelize deep learning models on Hadoop's YARN framework
• A comprehensive tutorial to distributed deep learning with Hadoop

Who This Book Is For

If you are a data scientist who wants to learn how to perform deep learning on Hadoop, this is the book for you. Knowledge of the basic machine learning concepts and some understanding of Hadoop is required to make the best use of this book.

What You Will Learn

• Explore Deep Learning and various models associated with it
• Understand the challenges of implementing distributed deep learning with Hadoop and how to overcome it
• Implement Convolutional Neural Network (CNN) with deeplearning4j
• Delve into the implementation of Restricted Boltzmann Machines (RBM)
• Understand the mathematical explanation for implementing Recurrent Neural Networks (RNN)
• Get hands on practice of deep learning and their implementation with Hadoop.

In Detail

This book will teach you how to deploy large-scale dataset in deep neural networks with Hadoop for optimal performance.

Starting with understanding what deep learning is, and what the various models associated with deep neural networks are, this book will then show you how to set up the Hadoop environment for deep learning. In this book, you will also learn how to overcome the challenges that you face while implementing distributed deep learning with large-scale unstructured datasets. The book will also show you how you can implement and parallelize the widely used deep learning models such as Deep Belief Networks, Convolutional Neural Networks, Recurrent Neural Networks, Restricted Boltzmann Machines and autoencoder using the popular deep learning library deeplearning4j.

Get in-depth mathematical explanations and visual representations to help you understand the design and implementations of Recurrent Neural network and Denoising AutoEncoders with deeplearning4j. To give you a more practical perspective, the book will also teach you the implementation of large-scale video processing, image processing and natural language processing on Hadoop.

By the end of this book, you will know how to deploy various deep neural networks in distributed systems using Hadoop.

Style and approach

This book takes a comprehensive, step-by-step approach to implement efficient deep learning models on Hadoop. It starts from the basics and builds the readers' knowledge as they strengthen their understanding of the concepts. Practical examples are included in every step of the way to supplement the theory.

Deep Learning with Hadoop

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Deep Learning with Hadoop

Copyright © 2017 Packt Publishing

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First published: February 2017

Production reference: 1130217

Published by Packt Publishing Ltd.

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ISBN 978-1-78712-476-9

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Credits

Authors Dipayan Dev	Copy Editor Safis Editing
Reviewers Shashwat Shriparv Wissem EL Khlifi	Project Coordinator Shweta H Birwatkar
Commissioning Editor Amey Varangaonkar	Proofreader Safis Editing
Acquisition Editor Divya Poojari	Indexer Mariammal Chettiyar
Content Development Editor Sumeet Sawant	Graphics Tania Dutta
Technical Editor Nilesh Sawakhande	Production Coordinator Melwyn Dsa

About the Author

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.

About the Reviewers

Shashwat Shriparv has more than 7 years of IT experience. He has worked with various technologies on his career path, such as Hadoop and subprojects, Java, .NET, and so on. He has experience in technologies such as Hadoop, HBase, Hive, Pig, Flume, Sqoop, Mongo, Cassandra, Java, C#, Linux, Scripting, PHP, C++, C, Web technologies, and various real-life use cases in BigData technologies as a developer and administrator. He likes to ride bikes, has interest in photography, and writes blogs when not working.

He has worked with companies such as CDAC, Genilok, HCL, UIDAI(Aadhaar), Pointcross; he is currently working with CenturyLink Cognilytics.

He is the author of Learning HBase, Packt Publishing, the reviewer of Pig Design Pattern book, Packt Publishing, and the reviewer of Hadoop Real-World Solution cookbook, 2nd edition.

I would like to take this opportunity to thank everyone who have somehow made my life better and appreciated me at my best and bared with me and supported me during my bad times.

Wissem El Khlifi is the first Oracle ACE in Spain and an Oracle Certified Professional DBA with over 12 years of IT experience. He earned the Computer Science Engineer degree from FST Tunisia, Masters in Computer Science from the UPC Barcelona, and Masters in Big Data Science from the UPC Barcelona. His area of interest include Cloud Architecture, Big Data Architecture, and Big Data Management & Analysis.

His career has included the roles of: Java analyst / programmer, Oracle Senior DBA, and big data scientist. He currently works as Senior Big Data and Cloud Architect for Schneider Electric / APC. He writes numerous articles on his website http://www.oracle-class.com and his twitter handle is @orawiss.

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Dedication

To my mother, Dipti Deb and father, Tarun Kumar Deb.

And also my elder brother, Tapojit Deb.

Preface

This book will teach you how to deploy large-scale datasets in deep neural networks with Hadoop for optimal performance.

Starting with understanding what deep learning is, and what the various models associated with deep neural networks are, this book will then show you how to set up the Hadoop environment for deep learning.

What this book covers

Chapter 1, Introduction to Deep Learning, covers how deep learning has gained its popularity over the last decade and is now growing even faster than machine learning due to its enhanced functionalities. This chapter starts with an introduction of the real-life applications of Artificial Intelligence, the associated challenges, and how effectively Deep learning is able to address all of these. The chapter provides an in-depth explanation of deep learning by addressing some of the major machine learning problems such as, The curse of dimensionality, Vanishing gradient problem, and the likes. To get started with deep learning for the subsequent chapters, the classification of various deep learning networks is discussed in the latter part of this chapter. This chapter is primarily suitable for readers, who are interested to know the basics of deep learning without getting much into the details of individual deep neural networks.

Chapter 2, Distributed Deep Learning for Large - Scale Data, explains that big data and deep learning are undoubtedly the two hottest technical trends in recent days. Both of them are critically interconnected and have shown tremendous growth in the past few years. This chapter starts with how deep learning technologies can be furnished with massive amount of unstructured data to facilitate extraction of valuable hidden information out of them. Famous technological companies such as Google, Facebook, Apple, and the like are using this large-scale data in their deep learning projects to train some aggressively deep neural networks in a smarter way. Deep neural networks, however, show certain challenges while dealing with Big data. This chapter provides a detailed explanation of all these challenges. The latter part of the chapter introduces Hadoop, to discuss how deep learning models can be implemented using Hadoop's YARN and its iterative Map-reduce paradigm. The chapter further introduces Deeplearning4j, a popular open source distributed framework for deep learning and explains its various components.

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