csinva/cookiecutter-ml-research
A logical, reasonably standardized, but flexible project structure for conducting ml research 🍪
This project provides a standardized framework for machine learning researchers to organize their algorithm design projects. It helps manage the iterative process of developing new ML algorithms, defining their architectures, and systematically comparing them against baselines across various hyperparameters. The output is well-structured code, experimental results, and analysis notebooks, primarily for data scientists and ML researchers.
Use this if you are developing new machine learning algorithms and need a robust, standardized way to manage experiments, hyperparameter sweeps, and result analysis.
Not ideal if you are solely focused on applying existing ML models or performing data analysis without the need for extensive algorithm development and experimental tracking.
Stars
18
Forks
5
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jan 23, 2026
Commits (30d)
0
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