Existing generalization bounds for deep neural networks require data to be
independent and identically distributed (iid). This assumption may not hold in
real-life applications such as evolutionary biology, infectious disease
epidemiology, and stock price prediction. This work establishes a
generalization bound of feed-forward neural networks for non-stationary
Ï•-mixing data