Computer Go

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Covers hand pose estimation, regression techniques, and the evolution of image classification models from LeNet to VGG19.

Solving Connect Four: A-B Pruning and Monte-Carlo Tree Search

Explores applying game theory to optimize strategies in Connect Four using advanced algorithms.

Connect Four: α-β Pruning vs Monte-Carlo Tree Search

Explores strategies to solve Connect Four using α-β pruning and Monte-Carlo tree search.

Solving Connect Four: Game Theory Strategies

Explores game theory strategies to solve Connect Four efficiently using minimax, alpha-beta pruning, and Monte Carlo methods.

Model-Based Deep Reinforcement Learning: Monte Carlo Tree Search

Explores model-based deep reinforcement learning, focusing on Monte Carlo Tree Search and its applications in game strategies and decision-making processes.

Reinforcement Learning: Q-Learning

Introduces Q-Learning, Deep Q-Learning, REINFORCE algorithm, and Monte-Carlo Tree Search in reinforcement learning, culminating in AlphaGo Zero.

Solving Connect Four: A-B Pruning and Monte-Carlo Tree Search

Explores solving Connect Four using game theory algorithms and compares their efficiency.

Solving Connect Four: A-B Pruning and Monte-Carlo Tree Search

Explores solving Connect Four using game theory and algorithms optimization, comparing minimax, alpha-beta pruning, and Monte-Carlo tree search.

Connect Four: Game Theory Approach

Explores solving Connect Four using game theory algorithms to find optimal strategies efficiently.

Connect Four: Alpha-Beta Pruning and Monte-Carlo Tree Search

Explores solving Connect Four using game theory algorithms and compares their performance.