Epoch
One complete pass through all the training data during model training.
Plain English Explanation
An epoch is one complete pass through all the training data. If a model sees every example once, that counts as one epoch. Training usually requires many epochs before the system performs well.
Too few epochs may underfit; too many without safeguards may overfit.
Analogy
An epoch is like reading an entire textbook cover to cover once. Re-reading it multiple times — multiple epochs — helps the material sink in, though eventually you stop gaining much from repetition.
How is it used?
When engineers report that a model was "trained for 50 epochs," they mean it reviewed the entire dataset fifty times — a standard way to describe how thoroughly a model has been trained.
Real-world Example
Training logs for image models often show loss decreasing over epochs until performance plateaus.
Common Misconceptions
More epochs are not always better — training too long on limited data can hurt generalisation.