It is common to use a validation set to reduce overfitting for machine learning tasks. For traditional (non-neural network based) models, many validation techniques, such as cross-validation, can be used to find the best hyperparameters for the model.
However, for deep learning models, as it requires extensive computation resources, brute force search-based validation techniques may not be applicable. How do we do validation for deep learning model? One possible approach is to use a validation set for early stopping, is there any other techniques?