5 interesting things (26/06/2019)

·1 min read

Checklist for debugging neural networks - well written trouble shooting for neural networks models which is not language or framework specific! https://towardsdatascience.com/checklist-for-debugging-neural-networks-d8b2a9434f21
Why Software Projects Take Longer Than You Think A Statistical Model - great post about a problem we all face. Usually we try to solve it using "instrumental changes" - changing methods \ processes \ ... . This post tries to show that there is more to it than just the behavioural change.
https://erikbern.com/2019/04/15/why-software-projects-take-longer-than-you-think-a-statistical-model.html
(and linear digressions episode about it - http://lineardigressions.com/episodes/2019/4/21/using-statistics-to-understand-why-your-software-projects-are-always-running-late)

Google What-If-Tool (WIT) - A nice tool by Google that was released few month ago. The terminology is actually a bit misleading and counterfactuals don't carry the meaning they have in causal inference. It is more like matching with two possible distance matrices - L1 and L2.
https://ai.googleblog.com/2018/09/the-what-if-tool-code-free-probing-of.html

causallib - New python causal inference package from IBM
https://github.com/IBM/causallib
There is also a python causal inference package from Microsoft which was released about a year ago - https://github.com/Microsoft/dowhy.

A Visual Intro to NumPy and Data Representation - What can I say, I really like Jay's guides -
https://jalammar.github.io/visual-numpy/