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
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Denoising autoencoder

The reconstruction of output from input does not always guarantee the desired output, and can sometimes end up in simply copying the input. To prevent such a situation, in [134], a different strategy has been proposed. In that proposed architecture, rather than putting some constraints in the representation of the input data, the reconstruction criteria is built, based on cleaning the partially corrupted input.

"A good representation is one that can be obtained robustly from a corrupted input and that will be useful for recovering the corresponding clean input."

A denoising autoencoder is a type of autoencoder which takes corrupted data as input, and the model is trained to predict the original, clean, and uncorrupted data as its output. In this section, we will explain the basic idea behind designing a denoising autoencoder.

Architecture of a Denoising autoencoder

The primary idea behind a denoising autoencoder is to introduce a corruption process, Q (k/ | k), and reconstruct...