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.