Mathematical Foundations
Linear algebra, probability, calculus and optimisation — the machinery every model is built on.
6concepts
30flashcards
54minutes of reading
- 01 Optimisation Theory Convexity, why SGD finds good solutions on non-convex losses, saddle points at scale, momentum as a damped oscillator, and learning-rate schedules as implicit regularisation.
- 02 Statistical Learning Theory Primer Bias-variance, PAC-learning, VC dimension, why deep nets break classical generalisation bounds, double descent, and what scaling laws are actually saying.