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
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Challenges of deep learning for big data

The potential of big data is certainly noteworthy. However, to fully extract valuable information at this scale, we would require new innovations and promising algorithms to address many of these related technical problems. For example, to train the models, most of the traditional machine learning algorithms load the data in memory. But with a massive amount of data, this approach will surely not be feasible, as the system might run out of memory. To overcome all these gritty problems, and get the most out of the big data with the deep learning techniques, we will require brain storming.

Although, as discussed in the earlier section, large-scale deep learning has achieved many accomplishments in the past decade, this field is still in a growing phase. Big data is constantly raising limitations with its 4Vs. Therefore, to tackle all of those, many more advancements in the models need to take place.

Challenges of deep learning due to massive volumes of...