Math Concepts Deep Dive - Expanded Notes

A concept-first deep dive into the math behind machine learning.

This guide is a conceptual reference and does not list individual projects. It explains the foundational math used across the broader ML project suite: algebra, functions, linear algebra, calculus, probability, and optimization.

What This Folder Contains

  • A concise index to the core concepts covered in MATH_CONCEPTS_DEEP_DIVE.md
  • No per-project expansions, because this guide is a theory-only deep dive

Concept Index

  • Algebra and equations
  • Functions and composition
  • Linear algebra (vectors, matrices, transforms)
  • Calculus (derivatives, gradients)
  • Probability and distributions
  • Optimization (gradient descent)

How to Use

  • Read the concept sections before attempting ML-focused project guides.
  • Use this as a reference when a concept appears in a project.