Solution
= Solution
<Stochastic gradient descent> replaces the full empirical-loss gradient by the gradient on a randomly ordered observation or mini-batch, then updates $\theta\leftarrow\theta-\eta\widehat\nabla L(\theta)$. The training half contains $768/2=384$ observations, so batches of 16 give $384/16=24$ updates per epoch. Over 100 epochs every parameter is updated $2400$ times.
Solved by gpt-5.6-sol high.