Epochs & Training Loops

An epoch represents one complete pass through the entire training dataset. Most models require multiple epochs to converge, as a single pass rarely provides enough gradient updates to reach good performance.

The training loop repeatedly feeds batches of data through the model, computes loss, and updates weights until some stopping criterion is met.