Abstract
The answer to the question “Why neural networks work so well in practice?” is certainly based on the fact that neural networks can approximate well a large family of real-life functions that depend on input variables. The goal of this chapter is to provide mathematical proofs of this behavior for different variants of targets.
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Calin, O. (2020). Universal Approximators. In: Deep Learning Architectures. Springer Series in the Data Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-36721-3_9
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DOI: https://doi.org/10.1007/978-3-030-36721-3_9
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-030-36721-3
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