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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CNNs, although not a new concept, has gained immense popularity in the last half a decade. The network primarily finds its application in the field of vision. The last few years have seen some major research on CNN by various technological companies such as Google, Microsoft, Apple, and the like, and also from various eminent researchers. Starting from the beginning, this chapter talked about the concept of convolution, which is the backbone of this type of network. Going forward, the chapter introduced the various layers of this network. Then it provided in-depth explanations for every associated layer of the deep CNN. After that, the various hyperparameters and their relations with the network were explained, both theoretically and mathematically. Later, the chapter talked about the approach of how to distribute the deep CNN across various machines with the help of Hadoop and its YARN. The last part discussed how to implement this network using Deeplearning4j for every worker working...