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

Backpropagation through time (BPTT)

You have already learnt that the primary requirement of RNNs is to distinctly classify the sequential inputs. The backpropagation of error and gradient descent primarily help to perform these tasks.

In case of feed forward neural networks, backpropagation moves in the backward direction from the final error outputs, weights, and inputs of each hidden layer. Backpropagation assigns the weights responsible for generating the error, by calculating their partial derivatives: where E denotes the error and w is the respective weights. The derivatives are applied on the learning rate, and the gradient decreases to update the weights so as to minimize the error rate.

However, a RNN, without using backpropagation directly, uses an extension of it, termed as backpropagation through time (BPTT). In this section, we will discuss BPTT to explain how the training works for RNNs.

Error computation

The backpropagation through time (BPTT) learning algorithm is a natural...