Book Image

Deep Learning with Hadoop

By : Dipayan Dev
Book Image

Deep Learning with Hadoop

By: Dipayan Dev

Overview of this book

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.

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Table of Contents (16 chapters)
Deep Learning with Hadoop
Deep Learning with Hadoop
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Dedication
Dedication
Preface
Preface
Free Chapter
1
Introduction to Deep Learning
Introduction to Deep Learning
Getting started with deep learning
Deep learning terminologies
Deep learning: A revolution in Artificial Intelligence
Classification of deep learning networks
Summary
2
Distributed Deep Learning for Large-Scale Data
Distributed Deep Learning for Large-Scale Data
Deep learning for massive amounts of data
Challenges of deep learning for big data
Distributed deep learning and Hadoop
Deeplearning4j - an open source distributed framework for deep learning
Setting up Deeplearning4j on Hadoop YARN
Summary
3
Convolutional Neural Network
Convolutional Neural Network
Understanding convolution
Background of a CNN
Basic layers of CNN
Distributed deep CNN
Convolutional layer using Deeplearning4j
Summary
4
Recurrent Neural Network
Recurrent Neural Network
What makes recurrent networks distinctive from others?
Recurrent neural networks(RNNs)
Backpropagation through time (BPTT)
Long short-term memory
Bi-directional RNNs
Distributed deep RNNs
RNNs with Deeplearning4j
Summary
5
Restricted Boltzmann Machines
Restricted Boltzmann Machines
Energy-based models
Boltzmann machines
Restricted Boltzmann machine
Convolutional Restricted Boltzmann machines
Deep Belief networks
Distributed Deep Belief network
Implementation using Deeplearning4j
Summary
6
Autoencoders
Autoencoders
Autoencoder
Sparse autoencoders
Deep autoencoders
Denoising autoencoder
Applications of autoencoders
Summary
7
Miscellaneous Deep Learning Operations using Hadoop
Miscellaneous Deep Learning Operations using Hadoop
Distributed video decoding in Hadoop
Large-scale image processing using Hadoop
Natural language processing using Hadoop
Summary
References
References

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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