My answer will probably not be as profound as your comment would deserve as I'm really not a mathematician. To the first part of your comment. You're right that the floating point numbers and non transcedent function are only approximated in the computer language, but I don't think it poses a problem for the nowadays programs/algorithms, because for instance in the case of neural networks there is much more unprecision inserted by the learning process itself (incorrect labels, noisy inputs). The whole process is local optima searching, and there are "only" statistical proves that it should lead to a good outcome (under many circumstances that in practice can't be assured). There is a problem with underflowing or overflowing numbers which in practice is solved by using the logarithm variants of the calculations.

And to the second part.. I don't know much (or actually anything) about Norman J. Wildberger - actually you introduced him to me, so I can't answer to the second part of your question. I'll try to follow him a bit more, from what I've just seen I like his view on mathematics where the next step in maths is chained on the last one.