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
Deep Learning involves extracting features and insights from multiple layers of the data. This book will teach you how to deploy the deep learning networks with Hadoop.
Starting with understanding what deep learning is and what the various models associated with deep learning 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 Hadoop. The book will also show you how you can implement and parallelize Deep Belief Networks, CNN, RNN, RBM and much more using the popular deep learning library deeplearning4j. Get in depth mathematical explanations, visual representations to understand the implementation of Denoising AutoEncoders with deeplearning4j. To give you a more practical perspective, the book will also teach you how you can implement image classification, audio processing and natural language processing on Hadoop.
By the end of this book, you will know how to deploy deep learning 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 is adept in large-scale data and especially have expertise in Hadoop Framework. He has completed his Post Graduation from National Institute of Technology Silchar, and now working as a software professional in Bangalore, India from past 2 years. His core interest lies in Big Data and Machine learning techniques. He’s also interested in a wide range of distributed system technologies, such as Redis, Apache Hadoop, 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. He is recently authored an book on “A Hand Book on Hadoop : Real World Approach for Infinite Scalability”. One of his biggest research “Dr.Hadoop” has recently been refereed by Goo Wikipedia in their Apache Hadoop article.
http://dipayandev.blogspot.in/
https://www.linkedin.com/in/dipayandev
http://hadooptutorials.co.in/tutorials/hadoop/big-data-analytics-what-is-that.html
http://link.springer.com/chapter/10.1007/978-3-319-27520-8_3
http://link.springer.com/article/10.1631/FITEE.1500015
