Visual explanations of vectors, transformations, matrix multiplication, eigenvectors, and the intuition behind linear algebra.
Grant Sanderson's renowned visual series transforms linear algebra from dry grid arithmetic into vivid geometric intuition. By animating basis vectors, linear transformations, determinants, dot products, change of basis, and eigenvectors, this series makes the geometry of neural network weight matrices and attention projections click immediately.
Course status is tracked independently from roadmap topic checkboxes so you can mark external courses without altering your topic mastery percentage.
Phase 1 Primary Starting Resource (Module 1.1)
All video lessons and interactive written companions are freely available without registration.
Status: Verified (2026-11-09)
Review these foundations before starting so you get the most out of this material:
In Phase 1: Mathematics for AI
Start Phase 1 here before diving into proof-heavy problem sets. Watch all chapters alongside Module 1.1 to build geometric intuition for matrix multiplication and eigen-decompositions.
A university-level linear algebra course covering vector spaces, matrices, linear systems, eigenvalues, and related topics.
Intuitive visual lessons explaining derivatives, integrals, and fundamental calculus concepts.
Mathematical foundations of machine learning, including linear algebra, vector calculus, probability, and optimization.