Deep learning algorithms are typically applied to extremely complicated domains where the true generation process essentially involves simulating the entire universe… Controlling the complexity of the model is not a simple matter of finding the model of the right size, with the right number of parameters. Instead, we might find—and indeed in practical deep learning scenarios, we almost always do find—that the best fitting model (in the sense of minimizing generalization error) is a large model that has been regularized appropriately