Students Professional
JA EN

#convexity

2 articles

01 ·Calculus & Optimization·★ MEMBER·PAPER·9 min read Convexity and Optimization — Why Deep Learning Works Even Though It Isn't Convex Optimization textbooks teach a stark divide: convex problems are solvable, non-convex ones come with no guarantees. So why does deep learning — whose loss surface is provably non-convex — work at all? From convex sets and functions to saddle points and flat minima, this article connects the whole story in the language of landscapes. 02 ·Calculus & Optimization·FREE·7 min read Calculus for AI — The Gradient Is an Arrow Saying Which Way Is Better No epsilon-delta limits, no integration by parts. Training is measuring a slope and stepping the other way. A derivative is a multiplier, a gradient is a list of slopes, the chain rule is multiplication — and Jacobians and Hessians only need to be recognised, not computed.