Epoch
One complete pass through the entire training dataset during optimization of a machine learning algorithm, allowing the model to update its internal weights.
Think of It Like This
Like reading through an entire textbook from cover to cover once before an exam; you might need to read it multiple times to fully understand it.
During a single epoch, the model processes every training example exactly once, usually in smaller batches. Training deep neural networks typically requires many epochs to reach convergence. However, running too many epochs can lead to overfitting, which is why early stopping is often employed to halt training when validation performance degrades.