 The increase in computational power that comes with deep net architecture brings with it additional dangers. In fact, the representational power of deep networks is so great that they can simply memorize the correct answer for every item in a large, complex data set, even if the “correct” labels were randomly assigned (Zhang et al. 2016 in  OIR).  The result is poor generalization of the task to be learned—with total failure to properly respond to inputs outside the training set. Effective deep nets thus employ an array of strategies to prevent them from merely memorizing training data, mostly by biasing the network against the learning of fine-grained idiosyncrasies. Popular options include dropout, which randomly deactivates a small number of nodes during training, and weight decay rules, which cause weights to decrease in value if not constantly refreshed by different examples.