Unfortunately I won't be able to provide a MWE, as it would be too big and complex, and this question will be based on pure semantics, basically (and I understand if this is impossible to answer in this way).
In any case, I have written a program which starts and trains a multilayer feedforward backpropagating neural network. I don't expect it to be or intend to make it useful in practice, as it is not optimized at all (and runs in my CPU) and I wouldn't know how to optimize it or if it is even possible in Mathematica. The point is that it is just an exercise.
Nonetheless, I would like to be able to run it on simple cases. It has already worked learning the OR and XOR logical operators, so by 'simple' I mean something a little more involved.
One of the things I believe to be spoiling its functionality (both in efficiency and in efficacy) are small numbers, as I get countless warnings of the type
"19.4806\ 2.440253060325*10^-412 is too small to represent as a
normalized machine number; precision may be lost"
Now it only appears on forms of powers of ten as I have replaced an appearing Exponential by a form which defaults to zero for small enough argument.
In any case, my question is: do you have any suggestion as to how deal with this globally? I have no idea what would be a convenient way to make Mathematica treat every number to a given precision (or if that even is what I want).