Today's Readings

I read voraciously, and, at some point, I decided it would be nice to share some of the stuff I read with the rest of the world. There were two worthy items today:

Peter Norvig, Director of Research at Google, discusses mistakes people make when they perform A/B tests. Plenty of useful insights, I have made (or seen people make) at least a half of these mistakes. The best observation, I think, was about people confusing uniformity with randomness.

Jake VanderPlas talks about estimating models with more parameters than available data points. Besides the main point, there is a nice illustration of what L1/L2 regularizations mean from a Bayesian perspective.