The Recipes 🍱 section is made up of small tutorials. Each one explains how to perform mundane tasks, such as measuring the performance of a model, selecting hyperparameters, etc.
The Examples 🌶️ section contains more involved notebooks with less explanations. Each notebook addresses a particular machine learning problem.
The API 📚 section references all the modules, classes, and functions in River. It is automatically generated from the codebase's Python docstrings.
Feel welcome to open a discussion if you have a question. Before that you can check out the FAQ 🙋, which has answers to recurring questions.
The released versions are listed in the Releases 🏗 section. Changes that will be part of the next release are listed in the unreleased section of the documentation's development version, which you may find here.
We recommend checking out Awesome Online Machine Learning if you want to go deeper. There you will find online machine learning related content: research papers, alternative and complementary software, blog posts, etc.