Imagine I want to consider the temperature for a process given several input varibales. The temperature can be anywhere between 400 and 500 K. Consider I have experimental data to train the network and then I want to predict the temperature for a test point. Consider the data as non linear.
As I understand the theory of NN activation function are needed to bound the value between 0 and 1. How could I proceed in my example? Should I just scale my data between 0 and 1? Are there good and bad methods to scale (of course there will be?). Or should I modify the activation function?
However, what if one of the training point's is at 600 K. If I bound the values it would be impossible for the NN to reach this value. I hope someone can give me clarity on this issues.