The Universal Approximation Theorem states that a feedforward neural network with a single hidden layer, using a non-linear activation function, can approximate any continuous function on a compact, given enough neurons. | Visual Math
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The Universal Approximation Theorem states that a feedforward neural network with a single hidden layer, using a non-linear activation function, can approximate any
Visual Math . . The Universal Approximation Theorem states that a feedforward neural network with a single hidden layer, using a non-linear activation function, can approximate any continuous function on a compact, given enough neurons.
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