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

Evaluating the sparse decomposition


The sparse autoencoder is also known as over-complete representation and has a higher number of nodes in the hidden layer. The sparse autoencoders are usually executed with the sparsity parameter (regularization), which acts as a constraint and restricts the node to being active. The sparsity can also be assumed as nodes dropout caused due to sparsity constraints. The loss function for a sparse autoencoder consists of a reconstruction error, a regularization term to contain the weight decay, and KL divergence for sparsity constraint. The following representation gives a very good illustration of what we are talking about:

Getting ready

  1. The dataset is loaded and set up.
  2. Install and load the autoencoder package using the following script:
install.packages("autoencoder")
require(autoencoder)

How to do it...

  1. The standard autoencoder code of TensorFlow can easily be extended to the sparse autoencoder module by updating the cost function. This section will introduce...