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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Long short-term memory

In this section, we will discuss a special unit called Long short-term memory (LSTM), which is integrated into RNN. The main purpose of LSTM is to prevent a significant problem of RNN, called the vanishing gradient problem.

Problem with deep backpropagation with time

Unlike the traditional feed forward network, due to unrolling of a RNN with narrow time steps, the feed forward network generated this way could be aggressively deep. This sometimes makes it extremely difficult to train via backpropagation through the time procedure.

In the first chapter, we discussed the vanishing gradient problem. An unfolded RNN suffers from the vanishing gradient problem of exploding while performing backpropagation through time.

Every state of a RNN depends on its input and its previous output multiplied by the current hidden state vector. The same operations happen to the gradient in the reverse direction during backpropagation through time. The layers and numerous time steps of the...