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

Building and comparing stochastic encoders and decoders


Stochastic encoders fall into the domain of generative modeling, where the objective is to learn join probability P(X) over given data X transformed into another high-dimensional space. For example, we want to learn about images and produce similar, but not exactly the same, images by learning about pixel dependencies and distribution. One of the popular approaches in generative modeling is Variational autoencoder (VAE), which combines deep learning with statistical inference by making a strong distribution assumption on h ~ P(h), such as Gaussian or Bernoulli. For a given weight W, the X can be sampled from the distribution as Pw(X|h). An example of VAE architecture is shown in the following diagram:

The cost function of VAE is based on log likelihood maximization. The cost function consists of reconstruction and regularization error terms:

Cost = Reconstruction Error + Regularization Error

The reconstruction error is how well we could...