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This was actually written back in 2013, but the topic of real-time analytics come up with regularity when talking with clients about new projects, so Dan’s thoughts still resonate strongly.
Andrew Gelman links to a post from the National Weather Service that clears up some confusion around the interpretation of weather forecast probabilities.
JAX: Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
JAX got a lot of attention at NeurIPS, as a way to put all of your Numpy calculations on a GPU or even TPU. I don’t have immediate use for this, but this will probably pop up all over other Python ML and deep learning projects.
I’ve really been enjoying Alex Andorra’s series on Bayesian stats. The guests are great, and unlike other “2 people chatting” podcast, Alex keeps this engaging by asking insightful questions.
Cullen Roche, who wrote the very good book “Pragmatic Capitalism”, put together the definitive reading list on “Money”.
Colorado seems to be in SkiMo fever, and I’ve been excited to be getting back into skiing. SkiMo might be a bit much (and gear extensive/expensive), but maybe SkiMo-lite?