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Deep Learning with Hadoop

You're reading from   Deep Learning with Hadoop Distributed Deep Learning with Large-Scale Data

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781787124769
Length 206 pages
Edition 1st Edition
Languages
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Author (1):
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Dipayan Dev Dipayan Dev
Author Profile Icon Dipayan Dev
Dipayan Dev
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Toc

Table of Contents (9) Chapters Close

Preface 1. Introduction to Deep Learning FREE CHAPTER 2. Distributed Deep Learning for Large-Scale Data 3. Convolutional Neural Network 4. Recurrent Neural Network 5. Restricted Boltzmann Machines 6. Autoencoders 7. Miscellaneous Deep Learning Operations using Hadoop 1. References

Deeplearning4j - an open source distributed framework for deep learning

Deeplearning4j (DL4J) [82] is an open source deep learning framework which is written for JVM, and mainly used for commercial grade. The framework is written entirely in Java, and thus, the name '4j' is included. Because of its use with Java, Deeplearning4j has started to earn popularity with a much wider audience and range of practitioners.

This framework is basically composed of a distributed deep learning library that is integrated with Hadoop and Spark. With the help of Hadoop and Spark, we can very easily distribute the model and Big datasets, and run multiple GPUs and CPUs to perform parallel operations. Deeplearning4j has primarily shown substantial success in performing pattern recognition in images, sound, text, time series data, and so on. Apart from that, it can also be applied for various customer use cases such as facial recognition, fraud detection, business analytics, recommendation engines...

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