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Key benefits

Get to grips with the deep learning concepts and set up Hadoop to put them to use

Implement and parallelize deep learning models on Hadoop’s YARN framework

A comprehensive tutorial to distributed deep learning with Hadoop

Description

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.

What you will learn

Explore Deep Learning and various models associated with it

Understand the challenges of implementing distributed deep learning with Hadoop and how to overcome it

Implement Convolutional Neural Network (CNN) with Deeplearning4j

Delve into the implementation of Restricted Boltzmann machines (RBMs)

Understand the mathematical explanation for implementing Recurrent Neural Networks (RNNs)

Understand the design and implementation of Deep Belief Networks (DBN) and Deep Autoencoders using Deeplearning4j

Get hands on practice of deep learning and their implementation with Hadoop.

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

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Oleg Okun Nov 05, 2018

If you are looking for a book to learn deeplearning4j - A Java based Distributed Deep Learning framework - this is the book to read. It contains a lot of useful code to immediately start working with, which implements the main Deep Learning models in deeplearning4j: Convolutional Neural Networks, Recurrent Neural Networks, Restricted Bolzman Machines, and Autoencoders. For beginners to Deep Learning, the author explains a network architecture for each model, its strong and weak points, details of fine-tuning to pay attention to. The last chapter sketches the design of real-world applications of the models described in the previous chapters, such as distributed video decoding and intelligent web browsing.

Amazon Verified review

JAYASMITA DEB Mar 14, 2017

A great book to know about distributed deep learning and is explained in a appropriate manner.

Amazon Customer Mar 14, 2017

I was looking for a good use case of Xanadu, a big data platform technology that I'm now commercializing, in Machine Learning.Distributed deep learning exploiting large-scale datasets that is explained in this book in detail will be one of best use cases of Xanadu,which can show Xanadu's excellent functionality in deep learning applications. This book is an excellent reference to anyone who wants toexplore the distributed deep learning for big data applications.

shreya dey Jul 23, 2017

Liked the book. Must read... Useful. Information overloaded!

Amazon Customer Jan 30, 2018

Simple and easy to understand....With useful information

Deep Learning with Hadoop: Distributed Deep Learning with Large-Scale Data

What do you get with eBook?