In a neural network, an epoch refers to one complete cycle through the entire training dataset. During an epoch, the neural network is trained with all the training data exactly once. An epoch is made up of one or more batches, where a batch is a subset of the training data used to train the neural network. The number of epochs is a hyperparameter that determines the number of times the entire dataset is worked through the learning algorithm. Every sample in the training dataset has a chance to update the internal model parameters once during an epoch. The number of epochs is traditionally large, often hundreds or thousands, allowing the learning algorithm to run until the error from the model has been sufficiently minimized.