There exists the Universal (Function) Approximation Theorem for neural networks — which states that they can represent/encode any function to a desired level of accuracy[0].
However there does not exist a theorem stating that those approximations can be learned (or how).
However there does not exist a theorem stating that those approximations can be learned (or how).
[0] https://en.m.wikipedia.org/wiki/Universal_approximation_theo...