Gradient Descent
The optimisation method that finds better parameters by repeatedly stepping downhill on the loss.
Gradient descent computes which direction each parameter should move to reduce loss, then takes a small step that way. Repeat millions of times and the model converges. Nearly all modern training uses a variant of it, usually Adam.
In practice: Like walking downhill in fog: you cannot see the valley, but you can feel the slope under your feet.