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in AI/ML, has compiled notes that prioritize intuition, context, and clear explanations, avoiding dense notation and outdated material.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 article announces a new discrete mathematics course available on the freeCodeCamp.org YouTube channel, taught by Karol Kurek. Discrete mathematics is crucial for fields like machine learning and algorithms, enabling tasks such as finding shortest paths, encryption, and data compression. The course provides an introduction to key areas including combinatorics, number theory, prime numbers, and concepts like the pigeonhole principle and Chinese remainder theorem.
It also includes practical applications and implementations in Python. The course aims to equip learners with a strong foundation for further exploration in this evolving field.
It also includes practical applications and implementations in Python. The course aims to equip learners with a strong foundation for further exploration in this evolving field.
A connection between descriptive set theory and computer science has been discovered, allowing problems in one field to be rewritten and solved in the other by Anton Bernshteyn.
Problems in descriptive set theory (measuring infinite graph colorings) are mathematically equivalent to problems in distributed algorithms (efficient network coloring).
Problems in descriptive set theory (measuring infinite graph colorings) are mathematically equivalent to problems in distributed algorithms (efficient network coloring).
Researchers have refined the simplex method, a key algorithm for optimization, proving it can't be improved further and providing theoretical reasons for its efficiency.
Mathematicians are using Srinivasa Ramanujan's century-old formulae to push the boundaries of high-performance computing and verify the accuracy of calculations.
