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A curated GitHub collection (Mathematics for Machine Learning) that organizes books, papers, video lectures, and math basics for learning the math behind machine learning, covering linear algebra, calculus, probability, statistics, and more.
Explains the mathematical concepts of gradient, Jacobian, and Hessian as fundamental tools in AI model training, describing how they measure change and their roles in optimization.
A paper proves that all elementary functions like sin, exp, log, sqrt can be generated from a single binary operator eml(x,y)=exp(x)-ln(y), similar to how NAND gates unify digital logic. This could simplify AI architectures by enabling a single trainable node for continuous mathematics.
This paper presents a calculus-based framework that uses first and second derivative tests to estimate the optimal vocabulary size hyper-parameter for end-to-end ASR systems, improving performance on the Librispeech corpus.
A personal blog post rigorously introducing the Riemann integral and proving the Fundamental Theorem of Calculus, including supporting theorems like Rolle’s and the Mean Value Theorem.