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 Liu is an associate professor of finance with tenure and the founding director of the Master of Science in Finance program at the University of Kentucky. He is the author of four books: Learn Generative AI with PyTorch (Manning Publications, 2024); AlphaGo Simplified (CRC Press, 2024); Machine Learning, Animated (CRC Press, 2023); and Make Python Talk (No Starch Press, 2021). His research interests include machine learning and corporate finance.
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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