csinva/cookiecutter-ml-research

A logical, reasonably standardized, but flexible project structure for conducting ml research 🍪

47
/ 100
Emerging

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.

machine-learning-research algorithm-development experimental-design hyperparameter-tuning model-comparison
No Package No Dependents
Maintenance 10 / 25
Adoption 6 / 25
Maturity 16 / 25
Community 15 / 25

How are scores calculated?

Stars

18

Forks

5

Language

Jupyter Notebook

License

MIT

Last pushed

Jan 23, 2026

Commits (30d)

0

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