In deep learning, the principles of generalization can become complicated and counterintuitive. While deep models have the capacity to severely overfit training data, the measures of their complexity are often implicit. Counterintuitively, larger network architectures with a greater number of parameters frequently demonstrate better generalization performance than smaller models.