 DNNs are prone to overfitting because of the added layers of abstraction, which allow them to model rare dependencies in the training data.Regularization methods such as Ivakhnenko's unit pruning or weight decay (formula_1-regularization) or sparsity (formula_2-regularization) can be applied during training to combat overfitting.Alternatively dropout regularization randomly omits units from the hidden layers during training.