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
Customer Feedback


Autoencoders, one of the most popular and widely applicable generative models, have been discussed in this chapter. Autoencoders basically help two phases: one is the encoder phase and the other is the decoder phase. In this chapter, we elaborated on both of these phases with suitable mathematical explanations. Going forward, we explained a special kind of autoencoder called the sparse autoencoder. We also discussed how autoencoders can be used in the world of deep neural networks by explaining deep autoencoders. Deep autoencoders consist of layers of Restricted Boltzmann machines, which take part in the encoder and decoder phases of the network. We explained how to deploy deep autoencoders using Deeplearning4j, by loading chunks of the input dataset into a Hadoop Distributed File System. Later in this chapter, we introduced the most popular form of autoencoder called the denoising autoencoder and its deep network version known as the stacked denoising autoencoder. The implementation...