This project is a machine learning study guide and technical knowledge base. It serves as a version-controlled repository of mathematical formulas and algorithmic explanations, providing instructional material and reference notes for the study of artificial intelligence.
The content is structured as a markdown-based knowledge base that pairs theoretical mathematical explanations directly with code implementations. This approach demonstrates model mechanics in practice across several specialized domains, including deep learning research, probabilistic graphical modeling, and reinforcement learning theory.
The curriculum covers a broad technical surface, including foundational machine learning mathematics, 3D computer vision geometry, and generative AI architectures. It also includes detailed material on probabilistic inference, optimization methods, and natural language processing.
