Let's say I am training a neural net (e.g. convolutional network or LSTM).
Generally, the longer the training (more epochs) leads to better accuracy, albeit at times at the expense of overfitting.
Another approach is to duplicate the data. Again, if I have 1000 examples, I could duplicate them 10 times and randomise them, and the neural net will learn better (but again, it may not be able to generalise well).
Is there any difference between duplicating data and adding epochs when training a neural net in terms of its learning?