What exactly is ML? How is it related to AI? Why is deep learning (DL) so popular these days? This book explains how traditional rule-based AI and ML work
What exactly is ML? How is it related to AI? Why is deep learning (DL) so popular these days? This book explains how traditional rule-based AI and ML work
Mark H. Liu is an Associate Professor of Finance, the (Founding) Director of the MS Finance Program at the University of Kentucky. He obtained his Ph.D. in finance from Boston College in 2004 and his M.A. in economics from Western University in Canada in 1998. Dr. Liu has more than 20 years of coding experience and is the author of two books: Make Python Talk (No Starch Press, 2021) and Machine Learning, Animated (CRC Press, 2023).
Inhaltsangabe
List of Figures Preface Acknowledgments Section I Rule-Based A.I. Chapter 1 Rule-Based AI in the Coin Game Chapter 2 Look-Ahead Search in Tic Tac Toe Chapter 3 Planning Three Steps Ahead in Connect Four Chapter 4 Recursion and MiniMax Tree Search Chapter 5 Depth Pruning in MiniMax Chapter 6 Alpha-Beta Pruning Chapter 7 Position Evaluation in MiniMax Chapter 8 Monte Carlo Tree Search Section II Deep Learning Chapter 9 Deep Learning in the Coin Game Chapter 10 Policy Networks in Tic Tac Toe Chapter 11 A Policy Network in Connect Four Section III Reinforcement Learning Chapter 12 Tabular Q-Learning in the Coin Game Chapter 13 Self-Play Deep Reinforcement Learning Chapter 14 Vectorization to Speed Up Deep Reinforcement Learning Chapter 15 A Value Network in Connect Four Section IV AlphaGo Algorithms Chapter 16 Implement AlphaGo in the Coin Game Chapter 17 AlphaGo in Tic Tac Toe and Connect Four Chapter 18 Hyperparameter Tuning in AlphaGo Chapter 19 The Actor-Critic Method and AlphaZero Chapter 20 Iterative Self-Play and AlphaZero in Tic Tac Toe Chapter 21 AlphaZero in Unsolved Games Bibliography
List of Figures Preface Acknowledgments Section I Rule-Based A.I. Chapter 1 Rule-Based AI in the Coin Game Chapter 2 Look-Ahead Search in Tic Tac Toe Chapter 3 Planning Three Steps Ahead in Connect Four Chapter 4 Recursion and MiniMax Tree Search Chapter 5 Depth Pruning in MiniMax Chapter 6 Alpha-Beta Pruning Chapter 7 Position Evaluation in MiniMax Chapter 8 Monte Carlo Tree Search Section II Deep Learning Chapter 9 Deep Learning in the Coin Game Chapter 10 Policy Networks in Tic Tac Toe Chapter 11 A Policy Network in Connect Four Section III Reinforcement Learning Chapter 12 Tabular Q-Learning in the Coin Game Chapter 13 Self-Play Deep Reinforcement Learning Chapter 14 Vectorization to Speed Up Deep Reinforcement Learning Chapter 15 A Value Network in Connect Four Section IV AlphaGo Algorithms Chapter 16 Implement AlphaGo in the Coin Game Chapter 17 AlphaGo in Tic Tac Toe and Connect Four Chapter 18 Hyperparameter Tuning in AlphaGo Chapter 19 The Actor-Critic Method and AlphaZero Chapter 20 Iterative Self-Play and AlphaZero in Tic Tac Toe Chapter 21 AlphaZero in Unsolved Games Bibliography
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