Overfitting can be addressed by techniques like “early-stopping” and regularizing the NN during the training. Early stopping checks the NN training performance in comparison to the NN validation performance at any given point in time. If the NN validation performance on the unseen data starts showing worse results from a certain point, we stop the NN training and fall back to the previous best state.In addition to early stopping, the use of weight regularization and dropout layers are effective in controlling overfitting. Start with relatively small base models when making a deep NN model architecture. Then add layers iteratively using either the grid search technique or one of the hyperparameter optimization methods we discussed earlier.