I'm new to data science and Neural Networks in general. Looking around, many people say it is better to normalize the data before doing anything with the NN. I understand how normalizing the input data can be useful.
However, I really don't see how normalizing the output data can help.
I've also tried both cases with an easy dataset, and I achieved the same results. The only difference is that in some weird problems, it is really hard to then re-convert the output back.
Can you give me some intuition on why we should also normalize the output?
Or maybe why it is indifferent?