A deep learning expert sets up a special rule to stop training early if the model's performance on new, unseen data does not get better after 10 tries. What is this special rule called?

The special rule described is called Early Stopping. Early Stopping is a regularization technique used in deep learning to prevent a model from overfitting, which occurs when a model learns the training data too well and loses its ability to generalize to new, unseen data. This rule works by monitoring the model's performance on a separate dataset called the validation set. The validation set consists of data the model has not been trained on, serving as the 'new, unseen data' mentioned. If the model's performance metric, typically validation loss or accuracy, does not improve for a predefined number of training iterations or epochs, known as 'patience,' the training process is halted. In this specific scenario, the '10 tries' represent the patience value, meaning training stops if performance on the validation data does not improve for 10 consecutive checks, thus ensuring the model maintains good performance on genuinely new data.