Universal Approximation Theorem


### at least 1 hidden layer
Any continuous function $f: [0, 1] \rightarrow [0, 1]$ can be approximated arbitrarily well by aneural network with at least 1 hidden layer with a finite number of weights
One wide (latent) layer is enough, but it is just a memorizer (Cybenko ’89)



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