I'm beginning with neural networks. Currently I'm struggling with fitting to a data-set, which has a large variance both in input variables.
It looks like this:
I1 I2 I3 O1 O2 O3 0.2 0.3 1500 1200 1100 1300 0.22 0.15 1200 1250 1110 1290
I think, that normalization of the data could help me with the numerical problems. But, I'm not sure, if I understand the very basic principle of normalization correctly.
My understanding is, that I'll simply transform all my input values to the range
[0,1], which will reduce the data variance. But, should I also normalize my outputs in the same way? Or should I normalize my inputs column-by-column and not altogether?
I've looked at the following questions, but I didn't find an answer to such a basic question: