What is Early Stopping and how does it prevent overfitting in Deep Learning?

Early stopping is a regularization technique that stops training when validation performance stops improving to prevent overfitting.

During training, neural networks initially improve on both training and validation data. After a point, validation performance may degrade while training performance continues improving.

This indicates overfitting.

Early stopping addresses this by monitoring validation metrics.

Mechanism:

Why it works:

Benefits: