Last week at DOAG 2017, I had two talks, one about deep learning with DL4J (slides here) and one about how to communicate uncertainty (or rather: how to construct prediction intervals for various methods / in various frameworks ranging from simple linear regression over Bayesian statistics to neural networks).

TLDR: The most important thing about communicating uncertainty is that you’re doing it.

Want all the formulae? presentation, github

🙂

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