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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.
Table des matières
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
A propos de l'auteur
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.
Résumé
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.
