How models actually learn, from vanilla gradient descent to Adam
Early stopping uses validation performance to decide when to stop training. If validation loss stops improving for long enough, you keep the best checkpoint and stop. Progress here is counted in epochs: one epoch is one full pass through the training data, and validation is typically checked after each one.
🔒 This is a Pro lesson — the interactive figure, worked examples, quiz and practice open with Pro access.
▶ Early Stopping