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

What makes recurrent networks distinctive from others?

You might be curious to know the specialty of RNNs. This section of the chapter will discuss these things, and from the next section onwards, we will talk about the building blocks of this type of network.

From Chapter 3 , Convolutional Neural Network, you have probably got a sense of the harsh limitation of convolutional networks and that their APIs are too constrained; the network can only take an input of a fixed-sized vector, and also generates a fixed-sized output. Moreover, these operations are performed through a predefined number of intermediate layers. The primary reason that makes RNNs distinctive from others is their ability to operate over long sequences of vectors, and produce different sequences of vectors as the output.


"If training vanilla neural nets is optimization over functions, training recurrent nets is optimization over programs"

 --Alex Lebrun

We show different types of input-output relationships of the neural...