# Should I use epochs > 1 when training data is unlimited?

If I have virtually endless training data (it's synthesized) is there still purpose in having epochs? I.e. training on the same samples multiple times?

However, if your data generating process is biased, for example if a variable X taking values in $\{0,1\}$ has its $0$ values generated first, it will quite probably be better to generate a finite sample and shuffle it, and then train with several epochs (possibly even shuffling between epochs. Otherwise, you will always "show" your $0$ examples before the $1$ examples, and after a long period of learning, the network will only have seen $1$'s in its recent past and thus never fitted to the $0$'s.