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

Deep Belief networks

Deep Belief networks (DBNs) were one of the most popular, non-convolutional models that could be successfully deployed as deep neural networks in the year 2006-07 [124] [125]. The renaissance of deep learning probably started from the invention of DBNs back in 2006. Before the introduction of DBNs, it was very difficult to optimize the deep models. By outperforming the Support Vector machines (SVMs), DBNs had shown that deep models can be really successful; although, compared to the other generative or unsupervised learning algorithms, the popularity of DBNs has fallen a bit, and is rarely used these days. However, they still play a very important role in the history of deep learning.


A DBN with only one hidden layer is just an RBM.

DBNs are generative models composed of more than one layer of hidden variables. The hidden variables are generally binary in nature; however the visible units might consist of binary or real values. In DBNs, every unit of each layer is...