/ml-cheatsheet

Machine learning cheatsheet

Primary LanguagePython

Machine Learning Cheatsheet

View The Cheatsheet

How To Contribute

  1. Clone Repo
git clone https://github.com/bfortuner/ml-glossary
  1. Install Dependencies
# Assumes you have the usual suspects installed: numpy, scipy, etc..
pip install sphinx sphinx-autobuild
pip install sphinx_rtd_theme
pip install recommonmark
  1. Preview Changes
cd ml-cheatsheet
cd docs
make html

  1. Verify your changes by opening the index.html file in _build/

  2. Submit Pull Request

Short for time?

Feel free to raise an issue to correct errors or contribute content without a pull request.

Style Guide

Each entry in the glossary MUST include the following at a minimum:

  1. Concise explanation - as short as possible, but no shorter
  2. Citations - Papers, Tutorials, etc.

Excellent entries will also include:

  1. Visuals - diagrams, charts, animations, images
  2. Code - python/numpy snippets, classes, or functions
  3. Equations - Formatted with Latex

The goal of the cheatsheet is to present content in the most accessible way possible, with a heavy emphasis on visuals and interactive diagrams. That said, in the spirit of rapid prototyping, it's okay to to submit a "rough draft" without visuals or code. We expect other readers will enhance your submission over time.

Why RST and not Markdown?

RST has more features. For large and complex documentation projects, it's the logical choice.

Top Contributors

We're big fans of Distill and we like their idea of offering prizes for high-quality submissions. We don't have as much money as they do, but we'd still like to reward contributors in some way for contributing to the glossary. In that spirit, we plan to publish a running table of top authors based on number and quality of original submissions:

Top Entries

Entry Author Link
Example Entry Your Name Your GitHub
Example Entry Your Name Your GitHub

Most Entries

Author Entries Link
Your Name 24 Your GitHub
Your Name 18 Your GitHub

We'd also like to publish top entries to our Medium Blog, for even more visibility. But in the end, this is an open-source project and we hope contributing to a repository of concise, accessible, machine learning knowledge is enough incentive on its own!

Tips and Tricks

  • Adding equations
  • Quickstart with Jupyter notebook template
  • Graphs and charts
  • Importing images
  • Linking to code

Resources