Universal approximation theorem
In the mathematical theory of artificial neural networks, universal approximation theorems are theorems of the following form: Given a family of neural networks, for each function
Universal approximation theorem - Wikipedia Jump to content From Wikipedia, the free encyclopedia Property of artificial neural networks This article may be too technical for most readers to understand . Please help improve it to make it understandable to non-experts , without removing the technical details. ( July 2023 ) ( Learn how and when to remove this message ) In the field of machine learning , the universal approximation theorems ( UATs ) state that neural networks with a certain structure can, in principle, approximate any continuous function to any desired degree of accuracy. These t
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