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