Title:
Lecture 2: Mathematics for Deep Neural Networks: Theory for shallow networks
Lecture 2: Mathematics for Deep Neural Networks: Theory for shallow networks
Author(s)
Schmidt-Hieber, Johannes
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Abstract
We start with the universal approximation theorem and discuss several proof strategies that provide some insights into functions that can be easily approximated by shallow networks. Based on this, a survey on approximation rates for shallow networks is given. It is shown how this leads to estimation rates. In the lecture, we also discuss methods that fit shallow networks to data.
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Date Issued
2019-03-08
Extent
58:56 minutes
Resource Type
Moving Image
Resource Subtype
Lecture