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Deep Learning with Hadoop

You're reading from  Deep Learning with Hadoop

Product type Book
Published in Feb 2017
Publisher Packt
ISBN-13 9781787124769
Pages 206 pages
Edition 1st Edition
Languages
Author (1):
Dipayan Dev Dipayan Dev
Profile icon Dipayan Dev

Table of Contents (16) Chapters

Deep Learning with Hadoop
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Dedication
Preface
Introduction to Deep Learning Distributed Deep Learning for Large-Scale Data Convolutional Neural Network Recurrent Neural Network Restricted Boltzmann Machines Autoencoders Miscellaneous Deep Learning Operations using Hadoop References

Distributed deep CNN


This section of the chapter will introduce some extremely aggressive deep CNN architecture, associated challenges for these networks, and the need of much larger distributed computing to overcome this. This section will explain how Hadoop and its YARN can provide a sufficient solution for this problem.

Most popular aggressive deep neural networks and their configurations

CNNs have shown stunning results in image recognition in recent years. However, unfortunately, they are extremely expensive to train. In the case of a sequential training process, the convolution operation takes around 95% of the total running time. With big datasets, even with low-scale distributed training, the training process takes many days to complete. The award winning CNN, AlexNet with ImageNet in 2012, took nearly an entire week to train on with two GTX 580 3 GB GPUs. The following table displays few of the most popular distributed deep CNNs with their configuration and corresponding time taken...

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