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Variational Inference: Bayesian Neural Networks 2016-2018 by Thomas Wiecki, updated by Maxim Kochurov Original blog post: https://twiecki.github.io/blog/2016/06/01/bayesian-deep-learning/ Current trends in Machine Learning There are currently three big trends in machine learning: Probabilistic Programming, Deep Learning and “Big Data”. Inside of PP, a lot of innovation is in making things scale us

When I give talks about probabilistic programming and Bayesian statistics, I usually gloss over the details of how inference is actually performed, treating it as a black box essentially. The beauty of probabilistic programming is that you actually don’t have to understand how the inference works in order to build models, but it certainly helps. When I presented a new Bayesian model to Quantopian’

A modern guide to getting started with Data Science and Python Python has an extremely rich and healthy ecosystem of data science tools. Unfortunately, to outsiders this ecosystem can look like a jungle (cue snake joke). In this blog post I will provide a step-by-step guide to venturing into this PyData jungle. What’s wrong with the many lists of PyData packages out there already you might ask? I

As you can see, we have multiple radon measurements (log-converted to be on the real line) in a county and whether the measurement has been taken in the basement (floor == 0) or on the first floor (floor == 1). Here we want to test the prediction that radon concentrations are higher in the basement. The Models Pooling of measurements Now you might say: “That’s easy! I’ll just pool all my data and