Introduction
A model's loss depends on many parameters at once — millions of them. To improve it, we need to know how the loss changes when each parameter moves. Partial derivatives answer this one variable at a time, and the gradient collects them into a single vector that points uphill. Gradient descent simply walks in the opposite direction.