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In theory you need two layers to model any function. In practice this is wildly different.


Any memoryless continuous function between two Euclidean spaces, I think you mean. The experts-and-manager model would need to be able to do more than that (as do most neural networks).

And part of the reason why single-hidden-layer networks aren't enough even in continuous memoryless Euclidean cases is, again, because of how loss functions work; you're unlikely to converge on a good approximation with very few hidden layers.




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