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-based learning


As the name suggests, the learning is augmented using a predefined model. Here, the model is represented in the form of transition probabilities and the key objective is to determine the optimal policy and value functions using these predefined model attributes (that is, TPMs). The policy is defined as a learning mechanism of an agent, traversing across multiple states. In other words, identifying the best action of an agent in a given state, to traverse to a next state, is termed a policy.

The objective of the policy is to maximize the cumulative reward of transitioning from the start state to the destination state, defined as follows, where P(s) is the cumulative policy P from a start state s, and R is the reward of transitioning from state st to state st+1 by performing an action at.

The value function is of two types: the state-value function and the state-action value function. In the state-value function, for a given policy, it is defined as an expected...