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 stacked autoencoders


The stacked autoencoder is an approach to train deep networks consisting of multiple layers trained using the greedy approach. An example of a stacked autoencoder is shown in the following diagram:

An example of a stacked autoencoder

Getting ready

The preceding diagram demonstrates a stacked autoencoder with two layers. A stacked autoencoder can have n layers, where each layer is trained using one layer at a time. For example, the previous layer will be trained as follows:

Training of a stacked autoencoder

The initial pre-training of layer 1 is obtained by training it over the actual input xi . The first step is to optimize the We(1) layer of the encoder with respect to output X. The second step in the preceding example is to optimize the weights We(2) in the second layer, using We(1) as input and output. Once all the layers of We(i) where i=1, 2, ...,n is number of layers are pretrained, model fine-tuning is performed by connecting all the layers together, as...