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

Introduction


Reinforcement Learning (RL) is an area in machine learning that is inspired by psychology, such as how agents (software programs) can take actions in order to maximize cumulative rewards.

The RL is reward-based learning where the reward comes at the end or is distributed during the learning. For example, in chess, the reward will be assigned to winning or losing the game whereas in games such as tennis, every point won is a reward. Some of the commercial examples of RL are DeepMind from Google uses RL to master parkour. Similarly, Tesla is developing AI-driven technology using RL. An example of reinforcement architecture is shown in the following figure:

Interaction of an agent with environment in Reinforcement Learning

The basic notations for RL are as follows:

  • T(s, a, s'): Represents the transition model for reaching state s' when action a is taken at state s
  • : Represents a policy which defines what action to take at every possible state
  • R(s): Denotes the reward received by agent...