Understanding deep neural networks

The O’Reilly Data Show Podcast: Michael Mahoney on developing a practical theory for deep learning.

By Ben Lorica
September 12, 2019
Understanding deep neural networks

Understanding deep neural networks
Data Show Podcast

00:00 / 00:39:31

In this episode of the Data Show, I speak with Michael Mahoney, a member of RISELab, the International Computer Science Institute, and the Department of Statistics at UC Berkeley. A physicist by training, Mahoney has been at the forefront of many important problems in large-scale data analysis. On the theoretical side, his works spans algorithmic and statistical methods for matrices, graphs, regression, optimization, and related problems. On the applications side, he has contributed to systems used for internet and social media analysis, social network analysis, as well as for a host of applications in the physical and life sciences. Most recently, he has been working on deep neural networks, specifically developing theoretical methods and practical diagnostic tools that should be helpful to practitioners who use deep learning.

Analyzing deep neural networks
Analyzing deep neural networks with WeightWatcher. Image by Michael Mahoney and Charles Martin, used with permission.

We had a great conversation spanning many topics, including:

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