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The Universal Approximation Theorems say that a neural network with at least one hidden layer and a suitable activation can approximate any continuous function on a compact domain as closely as you like.
An RKHS is a space of functions where evaluating a function at a point equals taking an inner product with a kernel section, which enables the โkernel trick.โ