Based on Garrett Thomas' lecture notes (Berkeley CS 189/289A). Rebuilt as an interactive learning experience: every concept paired with intuitive analogies, geometric pictures, multiple perspectives, live visualizations, and active-recall questions.
Each concept is explained algebraically, geometrically, computationally, and through its role in machine learning — so the idea sticks regardless of how you think.
Drag vectors, watch matrices warp space, see eigenvectors stay put, sculpt Gaussians, descend gradients — built directly into the page.
Every abstract idea grounded in physical, programming, and everyday analogies — so you build genuine intuition, not just notation.
Targeted questions after each topic. Wrong answers get patient, full explanations. Right answers get connected to the bigger picture.