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

Chapter 2.  Distributed Deep Learning for Large-Scale Data


"In God we trust, all others must bring data"

 --W. Edwards Deming

In this exponentially growing digital world, big data and deep learning are the two hottest technical trends. Deep learning and big data are two interrelated topics in the world of data science, and in terms of technological growth, both are critically interconnected and equally significant.

Digital data and cloud storage follow a generic law, termed as Moore's law [50], which roughly states that the world's data are doubling every two years; however, the cost of storing that data decreases at approximately the same rate. This profusion of data generates more features and verities, hence, to extract all the valuable information out of it, better deep learning models should be built.

This voluminous availability of data helps to bring huge opportunities for multiple sectors. Moreover, big data, with its analytic part, has produced lots of challenges in the field of...