If you have some input nodes containing fruit values like {apple=0, pear=1, oranges=2} vs temperature values like {5, 10, 30, 50}.
Is there any difference on how you set up the neural network to learn the output?
I'm guessing on the first case the neural network only learn about the input you used to train. For example if you then try with banana the result will be something random.
But in the case of temperature if I input 35 I would expect a result similar to 30.
Also, I'm guessing for the first case would need much more data to learn than the second case. So wondering if have to take different consideration on each case.