key book

Unlike tutorial repositories organized as learning paths, Key Book serves as a reference compendiumthe kind of resource practitioners consult when they need to quickly recall a formula, check a definition, or understand the relationship between two concepts without reading through a full textbook chapter.

Content is organized by topic arealinear algebra, probability theory, information theory, optimization, supervised learning, unsupervised learning, and deep learningwith each section providing precise mathematical statements alongside intuitive explanations.

Derivations are included for non-obvious results, and connections between related concepts are made explicit, helping readers build the coherent mental map that distinguishes practitioners who understand ML deeply from those who know only how to run standard workflows.

ML engineers preparing for technical interviews, graduate students studying for qualifying exams, and practitioners refreshing knowledge in areas outside their daily work use Key Book as a efficient reference that avoids the density of formal textbooks without sacrificing mathematical rigor.

The open-source format allows community contributions that expand coverage and correct errors, and the structured organization makes it useful both for focused lookup of specific topics and for broad review across the field.