Guides for statistical learning techniques.
Format: Jupyter notebooks
Possible sections:
- OVERVIEW: short blurb on the input, output, and overall goal of the technique
- THEORY: mathematical basis for the technique, include assumptions
- ALGORITHM: skech it, or point to reference
- EXAMPLE: one or more examples on applicable data
- RESOURCES: list useful references, helpful links
To view a .ipynb:
- Github attempts to render them behind the scenes -- this sometimes fails
- Alternative browser view: paste the notebook URL into https://nbviewer.jupyter.org/
- Otherwise download the file and open it locally using Jupyter via Python 3