This is an open, unconventional textbook covering mathematics, computing, and artificial intelligence from foundational principles. It's designed for practitioners seeking a deep understanding, moving beyond exam preparation and focusing on real-world application. The author, drawing from years of experience in AI/ML, has compiled notes that prioritize intuition, context, and clear explanations, avoiding dense notation and outdated material.
The compendium covers a broad range of topics, from vectors and matrices to machine learning, computer vision, and multimodal learning, with future chapters planned for areas like data structures and AI inference.
The compendium covers a broad range of topics, from vectors and matrices to machine learning, computer vision, and multimodal learning, with future chapters planned for areas like data structures and AI inference.
This discussion explores the effectiveness of simulated annealing compared to random search for optimizing a set of 16 integer parameters. The author seeks to determine if simulated annealing provides a significant advantage over random search, despite the parameter space being too large for exhaustive search. Responses suggest plotting performance over time and highlight the ability of simulated annealing to escape local optima as its main strength.
