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.
Table of Contents (16 chapters)
Deep Learning with Hadoop
About the Author
About the Reviewers
Customer Feedback

Deeplearning4j - an open source distributed framework for deep learning

Deeplearning4j (DL4J) [82] is an open source deep learning framework which is written for JVM, and mainly used for commercial grade. The framework is written entirely in Java, and thus, the name '4j' is included. Because of its use with Java, Deeplearning4j has started to earn popularity with a much wider audience and range of practitioners.

This framework is basically composed of a distributed deep learning library that is integrated with Hadoop and Spark. With the help of Hadoop and Spark, we can very easily distribute the model and Big datasets, and run multiple GPUs and CPUs to perform parallel operations. Deeplearning4j has primarily shown substantial success in performing pattern recognition in images, sound, text, time series data, and so on. Apart from that, it can also be applied for various customer use cases such as facial recognition, fraud detection, business analytics, recommendation engines, image and...