Book Image

Deep Learning with Hadoop

By : Dipayan Dev
Book Image

Deep Learning with Hadoop

By: Dipayan Dev

Overview of this book

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.
Table of Contents (16 chapters)
Deep Learning with Hadoop
About the Author
About the Reviewers
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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...