Early Stopping

• No of epochs is a hyper parameter : to prevent overfitting
• Early Stopping is a Regularization technique that improves image classification accuracy by intentionally stopping the training when validation loss increases. Training is stopped as training a model for too many epochs sometimes causes Overfitting.
• In Early Stopping, the number of epochs becomes a tunable hyperparameter. We continuously store the best parameters during training, and when these parameters no longer change for several epochs, we stop training.