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.