Parameters are updated without computing gradients, by directly adjusting weights toward the minimum.

Parameters are updated only once per epoch, after computing gradients on a mini-batch.

B) Parameters are updated after computing the gradient on just one randomly selected training example at a time, leading to faster initial progress but noisy convergence that oscillates near the minimum.

Parameters are updated after computing the gradient using the entire training dataset, leading to stable, deterministic convergence.