In a nutshell
Gradient descent is how most models learn. It measures the loss — a single number for how wrong the model currently is — then nudges the model's numbers in the direction that lowers the loss fastest, and repeats. The size of each nudge is set by the learning rate: too small and training crawls, too large and it overshoots the bottom and bounces instead of settling.