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

Distributed deep learning and Hadoop

From the earlier sections of this chapter, we already have enough insights on why and how the relationship of deep learning and big data can bring major changes to the research community. Also, a centralized system is not going to help this relationship substantially with the course of time. Hence, distribution of the deep learning network across multiple servers has become the primary goal of the current deep learning practitioners. However, dealing with big data in a distributed environment is always associated with several challenges. Most of those are explained in-depth in the previous section. These include dealing with higher dimensional data, data with too many features, amount of memory available to store, processing the massive Big datasets, and so on. Moreover, Big datasets have a high computational resource demand on CPU and memory time. So, the reduction of processing time has become an extremely significant criterion. The following are the...