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

Energy-based models

The main goal of deep learning and statistical modeling is to encode the dependencies between variables. By getting an idea of those dependencies, from the values of the known variables, a model can answer questions about the unknown variables.

Energy-based models (EBMs) [120] gather and collect the dependencies by identifying scaler energy, which generally is a measure of compatibility to each configuration of the variable. In EBMs, the predictions are made by setting the value of observed variables and finding the value of the unobserved variables, which minimize the overall energy. Learning in EBMs consists of formulating an energy function, which assigns low energies to the correct values of unobserved variables and higher energies to the incorrect ones. Energy-based learning can be treated as an alternative to probabilistic estimation for classification, decision-making, or prediction tasks.

To give a clear idea about how EBMs work, let us look at a simple example...