Deep learning methods operate in regimes that defy the traditional statistical mindset. Despite the non-convexity of empirical risks and the huge complexity of neural network architectures, stochastic gradient algorithms can often find the global minimizer of the training loss and achieve small generalization error on test data. As one possible explanation to the training efficiency of neural...