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Eric Ma goes deep on the math used in computational Bayesian statistics and probabilistic programming. I’m still working through it, but this already looks like a great resource for building a deeper understanding on what goes on when you press the inference button.
Say what you will about Google and their near monopoly in search, but I love how they’re using advances in NLP to make search results even more relevant. It’s subtle (from a user perspective) but good product management.
Great post on applying fairly standard ML techniques, combined with solid domain expertise to create a very useful data product. This is a nice template to use in applied data science.
I saw this in Gitlab’s Data Team Handbook1, and love the simplicity of it.
At the end of each work day, write down the six most important things you need to accomplish tomorrow. Do not write down more than six tasks. Prioritize those six items in order of their true importance. When you arrive tomorrow, concentrate only on the first task. Work until the first task is finished before moving on to the second task. Approach the rest of your list in the same fashion. At the end of the day, move any unfinished items to a new list of six tasks for the following day. Repeat this process every working day.