[29] Tengyu Ma - Non-convex Optimization For Machine Learning The Thesis Review podcast

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Science Thesis Review Sean Welleck

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[29] Tengyu Ma - Non-convex Optimization for Machine Learning

3y ago 1:17:22

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Innehåll tillhandahållet av The Thesis Review and Sean Welleck. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av The Thesis Review and Sean Welleck eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.

Tengyu Ma is an Assistant Professor at Stanford University. His research focuses on deep learning and its theory, as well as various topics in machine learning. Tengyu's PhD thesis is titled "Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding", which he completed in 2017 at Princeton University. We discuss theory in machine learning and deep learning, including the 'all local minima are global minima' property, overparameterization, as well as perspectives that theory takes on understanding deep learning. - Episode notes: https://cs.nyu.edu/~welleck/episode29.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview

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47 episoder

#Science #Thesis Review #Sean Welleck

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[29] Tengyu Ma - Non-convex Optimization for Machine Learning

The Thesis Review

published 3y ago

Dela

MP3•Episod hem

Innehåll tillhandahållet av The Thesis Review and Sean Welleck. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av The Thesis Review and Sean Welleck eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.

Tengyu Ma is an Assistant Professor at Stanford University. His research focuses on deep learning and its theory, as well as various topics in machine learning. Tengyu's PhD thesis is titled "Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding", which he completed in 2017 at Princeton University. We discuss theory in machine learning and deep learning, including the 'all local minima are global minima' property, overparameterization, as well as perspectives that theory takes on understanding deep learning. - Episode notes: https://cs.nyu.edu/~welleck/episode29.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview

… continue reading

47 episoder

#Science #Thesis Review #Sean Welleck

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