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HomeMIT 6.7960 Deep Learning, Fall 2024Lec 03. Approximation Theory
MIT 6.7960 Deep Learning, Fall 2024
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Lec 03. Approximation Theory
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Lec 04. Architectures: Grids
How well can you approximate a given function by a DNN? We will explore various facets of this issue, from universal approximation to Barron’s theorem. And does increasing the depth provably help for expressivity?