Fundamentals Of Reinforcement Learning
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Fundamentals of Reinforcement Learning
Artificial intelligence (AI) applications bring agility and modernity to our lives, and the reinforcement learning technique is at the forefront of this technology. It can outperform human competitors in strategy games, creative compositing, and autonomous movement. Moreover, it is just starting to transform our civilization. This book provides an introduction to AI, specifies machine learning techniques, and explores various aspects of reinforcement learning, approaching the latest concepts in a didactic and illustrated manner. It is aimed at students who want to be part of technological advances and professors engaged in the development of innovative applications, helping with academic and industrial challenges. Understanding the Fundamentals of Reinforcement Learning will allow you to: Understand essential AI concepts Gain professional experience Interpret sequential decision problems and solve them with reinforcement learning Learn how the Q-Learning algorithm works Practice with commented Python code Find advantageous directions
Deep Reinforcement Learning
Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance. Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailed explanations. The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. It also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications.
Fundamentals of Machine Learning for Predictive Data Analytics, second edition
The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.