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

Restricted Boltzmann machine

The Restricted Boltzmann machine (RBM) is a classic example of building blocks of deep probabilistic models that are used for deep learning. The RBM itself is not a deep model but can be used as a building block to form other deep models. In fact, RBMs are undirected probabilistic graphical models that consist of a layer of observed variables and a single layer of hidden variables, which may be used to learn the representation for the input. In this section, we will explain how the RBM can be used to build many deeper models.

Let us consider two examples to see the use case of RBM. RBM primarily operates on a binary version of factor analysis. Let us say we have a restaurant, and want to ask our customer to rate the food on a scale of 0 to 5. In the traditional approach, we will try to explain each food item and customer in terms of the variable's hidden factors. For example, foods such as pasta and lasagne will have a strong association with the Italian factors...