All the Python packages presented in this blog:
- nnetsauce Statistical/Machine Learning using Randomized and Quasi-Randomized (neural) networks | feedback form
- querier A query language for Python Data Frames | feedback form
- mlsauce Miscellaneous Statistical/Machine Learning stuff | feedback form
- teller Model-agnostic Statistical/Machine Learning explainability | feedback form
Now have a common home for their documentation, available on Techtonique website (it’s a work in progress).
Figure: New Techtonique Website
For this documentation, I’m using MkDocs in conjunction with keras-autodoc. With MkDocs, I found out that you can create a static website rapidly using Markdown. Regarding package technical documentation in particular, one thing that I find useful and that I’ve been searching for a while, is the ability for a tool to loop and read Python docstrings: that’s what keras-autodoc allowed me to do. I’ve heard of a way of doing such a thing for R documentation recently: