Validate neural networks without data with Dr. Charles Martin (Ep. 70)

Episode 65 of the Data Science at Home podcast, hosted by Francesco Gadaleta, titled "Validate neural networks without data with Dr. Charles Martin (Ep. 70)" was published on July 23, 2019 and runs 44 minutes.

July 23, 2019 ·44m · Data Science at Home

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Summary

In this episode, I am with Dr. Charles Martin from Calculation Consulting a machine learning and data science consulting company based in San Francisco. We speak about the nuts and bolts of deep neural networks and some impressive findings about the way they work. The questions that Charles answers in the show are essentially two: Why is regularisation in deep learning seemingly quite different than regularisation in other areas on ML? How can we dominate DNN in a theoretically principled way? References The WeightWatcher tool for predicting the accuracy of Deep Neural Networks https://github.com/CalculatedContent/WeightWatcher Slack channel https://weightwatcherai.slack.com/ Dr. Charles Martin Blog http://calculatedcontent.com and channel https://www.youtube.com/c/calculationconsulting Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning - Charles H. Martin, Michael W. Mahoney

Episode Description

The questions that Charles answers in the show are essentially two:

Why is regularisation in deep learning seemingly quite different than regularisation in other areas on ML?
How can we dominate DNN in a theoretically principled way?

References

The WeightWatcher tool for predicting the accuracy of Deep Neural Networks https://github.com/CalculatedContent/WeightWatcher
Slack channel https://weightwatcherai.slack.com/
Dr. Charles Martin Blog http://calculatedcontent.com and channel https://www.youtube.com/c/calculationconsulting
Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning - Charles H. Martin, Michael W. Mahoney