Deep Learning with Hadoop

Key Features

• 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

Book Description

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.

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.

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. 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 dipayandev.

Table of Contents

1. Introduction to Deep Learning
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
8. References

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