Gradient Descent & Optimization

> Five chapters have built toward one algorithm: compute a gradient, take a small step against it, repeat. This chapter runs it — and runs it badly on purpose, until a loss function explodes to nineteen million, to prove exactly why the step size matters as much as the gradient itself.