[D] Fourier transform vs NNs as function approximators

So this is probably a basic question. If the main premise of neural networks is that they are global function approximators, what advantage do they have against other approximators such Fourier transform, which is also proven to be able to approximate any function. Why does not the whole supervised learning field become one of calculating Fourier coefficients

machinelearning nns