The main training loop for a deep learning model executes a systematic, iterative process to optimize parameters $\mathbf{w}$ and $b$. During each epoch, the loop passes through the entire training dataset. For every iteration within an epoch, a minibatch $\mathcal{B}$ is processed. The model computes the loss for this minibatch and calculates the gradients of the loss with respect to each parameter using $\mathbf{g} \leftarrow \partial_{(\mathbf{w},b)} \frac{1}{|\mathcal{B}|} \sum_{i \in \mathcal{B}} l(\mathbf{x}^{(i)}, y^{(i)}, \mathbf{w}, b)$. Finally, the optimization algorithm updates the parameters using the rule $(\mathbf{w}, b) \leftarrow (\mathbf{w}, b) - \eta \mathbf{g}$. This cycle repeats until the training is complete.