L1 Regularization, L2 Regularization, Regularization, Early Stopping, Dropout.
absolute values of the weights to the loss function. This encourages some weights to become exactly zero, effectively performing feature selection. It’s a handy tool when you suspect that only a subset of your features is essential. L2 regularization, on the other hand, adds the square of the