same or different number of epochs for dataset of different sizes? I am trying to fit a DL model on a dataset, after a while of parameter tuning, I have determined the optimal number of epochs.  Now suppose there are new data available and I would like to include them to re-train the model, would I need to set the number of epochs to be exactly the same, or make it smaller such that I have equal number of gradient updates?  Both seem to make sense to me, but I am not sure which would usually work better.
I understand that a better way would be to use train-test splits to determine best epochs, but since data are super limited, I would prefer not to reserve any data if possible.
Any idea or suggestion would be much appreciated, thank you!
More data could potentially lead to more epochs required to find the best model. The number of epochs to reach the minimum loss will vary depending on your hyperparameters, dataset, and initial weights. I would get out of the mindset of trying to find the exact number of epochs required. Most popular deep learning libraries will allow you to stop training if the loss converges after a certain length of epochs. Additionally, if you overfit, you can create a callback function to save the best model only.