Ashwin Rao – författare

Foundations of Reinforcement Learning with Applications in Finance aims to demystify Reinforcement Learning, and to make it a practically useful tool for those studying and working in applied areas — especially finance.

Reinforcement Learning is emerging as a powerful technique for solving a variety of complex problems across industries that involve Sequential Optimal Decisioning under Uncertainty. Its penetration in high-profile problems like self-driving cars, robotics, and strategy games points to a future where Reinforcement Learning algorithms will have decisioning abilities far superior to humans. But when it comes getting educated in this area, there seems to be a reluctance to jump right in, because Reinforcement Learning appears to have acquired a reputation for being mysterious and technically challenging.

This book strives to impart a lucid and insightful understanding of the topic by emphasizing the foundational mathematics and implementing models and algorithms in well-designed Python code, along with robust coverage of several financial trading problems that can be solved with Reinforcement Learning. This book has been created after years of iterative experimentation on the pedagogy of these topics while being taught to university students as well as industry practitioners.

Features

To access the code base for this book, please go to: https://github.com/TikhonJelvis/RL-book

Foundations of Reinforcement Learning with Applications in Finance aims to demystify Reinforcement Learning, and to make it a practically useful tool for those studying and working in applied areas — especially finance.

Reinforcement Learning is emerging as a powerful technique for solving a variety of complex problems across industries that involve Sequential Optimal Decisioning under Uncertainty. Its penetration in high-profile problems like self-driving cars, robotics, and strategy games points to a future where Reinforcement Learning algorithms will have decisioning abilities far superior to humans. But when it comes getting educated in this area, there seems to be a reluctance to jump right in, because Reinforcement Learning appears to have acquired a reputation for being mysterious and technically challenging.

This book strives to impart a lucid and insightful understanding of the topic by emphasizing the foundational mathematics and implementing models and algorithms in well-designed Python code, along with robust coverage of several financial trading problems that can be solved with Reinforcement Learning. This book has been created after years of iterative experimentation on the pedagogy of these topics while being taught to university students as well as industry practitioners.

Features

To access the code base for this book, please go to: https://github.com/TikhonJelvis/RL-book

This book is a comprehensive guide for dentists on the practical use of midazolam in Pediatric Dentistry. It discusses predictable ways to achieve minimal/moderate sedation with midazolam. Midazolam is one of the most effective anxiolytic drugs and the sedative of choice in pediatric patients, especially for those of a certain age, extreme anxiety, and an inability to comprehend instructions. This book covers various aspects of midazolam sedation including pre-operative assessment, case selection, different routes of administration, intraoperative protocol, emergency management and meticulous documentation. It explains the importance of its conjunction with sound fundamentals of non-pharmacological behavior guidance and good local anesthetic techniques. Readers will learn how the rapid absorption, onset of action and fast metabolism makes midazolam an ideal short acting agent for dental procedures in children and a valuable asset in the child management armamentarium of dentists treating children.