#loss-surface

1 articles

01 ·Calculus & Optimization·FREE·PAPER·10 min read Gradient Descent from Scratch — The Geometry of Learning Every model that learns is walking downhill on a surface it cannot see all of. This article builds gradient descent from that one picture — the loss surface, the gradient as steepest ascent, the update rule w ← w − η∇L — then works out why the learning rate has a hard ceiling, why ravines make it zigzag, and what local minima really cost in high dimensions.