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

Performing model-free learning


Unlike model-based learning, where dynamics of transitions are explicitly provided (as transition probabilities from one state to another state), in model-free learning, the transitions are supposed to be deduced and learned directly from the interaction between states (using actions) rather explicitly provided. Widely used frameworks of mode-free learning are Monte Carlo methods and the Q-learning technique. The former is simple to implement but convergence takes time, whereas the latter is complex to implement but is efficient in convergence due to off-policy learning.

Getting ready

In this section, we will implement the Q-learning algorithm in R. The simultaneous exploration of the surrounding environment and exploitation of existing knowledge is termed off-policy convergence. For example, an agent in a particular state first explores all the possible actions of transitioning into next states and observes the corresponding rewards, and then exploits current...