An epoch is the number of cycles for which a model is trained. One epoch is when a whole dataset is passed forward and backward only once through the neural network. We can also say that an epoch is an easy way to track the number of cycles, while the training or validation error continues to go on. Since one epoch is too large to feed at once to the machine, we divide it into many smaller batches.
One of the techniques to do this is to use the early stopping Keras callback, which stops the training process if the training/validation error has not improved in the past 10 to 20 epochs.
