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

Setting up autoencoders


There exist a lot of different architectures of autoencoders distinguished by cost functions used to capture data representation. The most basic autoencoder is known as a vanilla autoencoder. It's a two-layer neural network with one hidden layer the same number of nodes at the input and output layers, with an objective to minimize the cost function. The typical choices, but not limited to, for a loss function are mean square error (MSE) for regression and cross entropy for classification. The current approach can be easily extended to multiple layers, also known as multilayer autoencoder.

The number of nodes plays a very critical role in autoencoders. If the number of nodes in the hidden layer is less than the input layer then an autoencoder is known as an under-complete autoencoder. A higher number of nodes in the hidden layer represents an over-complete autoencoder or sparse autoencoder.

The sparse autoencoder aims to impose sparsity in the hidden layer. This sparsity...