Book Image

R Deep Learning Cookbook

By : PKS Prakash, Achyutuni Sri Krishna Rao
Book Image

R Deep Learning Cookbook

By: PKS Prakash, Achyutuni Sri Krishna Rao

Overview of this book

Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance. By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Backward or reconstruction phase of RBM


In the reconstruction phase, the data from the hidden layer is passed back to the visible layer. The hidden layer vector of probabilities h0 is multiplied by the transpose of the weight matrix W and added to a visible layer bias vb, which is then passed through a sigmoid function to generate a reconstructed input vector prob_v1.

A sample input vector is created using the reconstructed input vector, which is then multiplied by the weight matrix W and added to the hidden bias vector hb to generate an updated hidden vector of probabilities h1.

This is also called Gibbs sampling. In some scenarios, the sample input vector is not generated and the reconstructed input vector prob_v1 is directly used to update the hi

Getting ready

This section provides the requirements for image reconstruction using the input probability vector.

  • mnist data is loaded in the environment
  • The RBM model is trained using the recipe Training a Restricted Boltzmann machine

How to do it...