There is a scaler in scikit-learn called the "StandardScaler". The name might imply that it is standard or fairly basic., but once you consider the implementation details that are required for all the edge cases the it's actually far from "standard" . The goal of this video is to explain why. Hopefully, by the end of this video, you'll appreciate all the tiny details that scikit-learn handles under the hood for you.
00:00 Introduction
01:37 Documentation
07:03 Online Learning
10:43 Numerical Issues
14:11 Source Code
Documentation for the standard scaler can be found here:
scikit-learn.org/stable/modul...
These deep dives are very useful! Thanks Vincent! I always tell my team to leverage existing tools, sometimes we like to think "oh, it's not that hard to implement X". And most of the time it actually isn't. However, we find ourselves getting to these edge cases fairly quickly, and then it becomes a whole thing. I'd love to see some scikit-lego related stuff.
Oh my go dthat shade of blue and orange brings back memories of my data science course ahhhhh matplotlib in my dreams >.
Now do random numbers drawn from a distribution with Pareto-tails with tail exponent < 2 ;)