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 djalil.chafai.net
A probabilistic proof of the Schoenberg theorem - Libres pensées d'un mathématicien ordinaire
| | cgad.ski
When Gradient Descent Is a Kernel Method
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| | fabricebaudoin.blog
Lecture 8. Sobolev inequalities on Dirichlet spaces: The Varopoulos approach | Research and Lecture notes
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| | In this lecture, we studySobolev inequalities on Dirichlet spaces. The approach we develop is related to Hardy-Littlewood-Sobolev theory The link between the Hardy-Littlewood-Sobolev theory and heat kernel upper bounds is due to Varopoulos, but the proof I give below I learnt it from my colleague RodrigoBañuelos. It bypasses the Marcinkiewicz interpolation theorem,that was originally used...
| | windowsontheory.org
A blitz through classical statistical learning theory - Windows On Theory
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| | Previous post: ML theory with bad drawings Next post: What do neural networks learn and when do they learn it, see also all seminar posts and course webpage. Lecture video (starts in slide 2 since I hit record button 30 seconds too late - sorry!) - slides (pdf) - slides (Powerpoint with ink and animation)...
| | www.hamza.se
Neural Networks: Simpler Than You Think | Hamza Khuswan
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| A walkthrough of implementing a neural network from scratch in Python, exploring what makes these seemingly complex systems actually quite straightforward.