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A blitz through classical statistical learning theory

Windows on Theory · published 31 January 2021

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ML Theory with bad drawings Read at the source ↗
windowsontheory.org
What do deep networks learn and when do they learn it Read at the source ↗
windowsontheory.org
https://windowsontheory.org/category/ml-theory-seminar/?order=asc
windowsontheory.org
https://boazbk.github.io/mltheoryseminar/cs229br.html
boazbk.github.io
https://harvard.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=5c6a9e86-bca7-42df-a04a-acc200ed2c2d
harvard.hosted.panopto.com
https://boazbk.github.io/mltheoryseminar/lectures/seminar_lecture1.pdf
boazbk.github.io
https://boazbk.github.io/mltheoryseminar/lectures/seminar_lecture1.pptx
boazbk.github.io
A Recipe for Training Neural Networks Read at the source ↗
karpathy.github.io
https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/
cs.huji.ac.il
https://mlstory.org/
mlstory.org
https://www.google.com/search?q=plot+x%5E2&oq=plot+x%5E2
google.com
https://arxiv.org/abs/1912.02178
arxiv.org
https://arxiv.org/abs/2010.11924
arxiv.org
https://arxiv.org/abs/1611.03530
arxiv.org
Understanding generalization requires rethinking deep learning? Read at the source ↗
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https://arxiv.org/abs/1812.11118
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Deep Double Descent (cross-posted on OpenAI blog) Read at the source ↗
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