Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A collaborative list of interactive Machine Learning, Deep Learning and Statistics websites
| Date | Stars |
|---|---|
| 2026-07-31 | 453 |
| 2026-08-01 | 453 |
| 2026-08-06 | 453 |
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growth rate 0.00%/day
# interactive-machine-learning-list A collaborative list of interactive Machine Learning, Deep Learning and Statistics websites. Started by [Piotr Migdał](https://p.migdal.pl/), but anyone is encouraged to contribute! It is a simple no-build Vue.js website: * [p.migdal.pl/interactive-machine-learning-list/](https://p.migdal.pl/interactive-machine-learning-list/) Feel invited to Pull Request other interactive visualizations (check [websites.yaml](https://github.com/stared/interactive-machine-learning-list/blob/master/websites.yaml))! :) ...aaand if you want to create such visualizations by yourself, see [In Browser AI](https://inbrowser.ai/). ## What goes there? Still I am thinking what is the best criterion. For sure things that are front-end (i.e. JavaScript within browser). For things using backend (when you can see solution, but it uses some PyTorch/TF/etc code on a server) I am still debating, but I lean on being more inclusive. In this context: * make sure it has some didactic value (otherwise ALL services using ML would qualify) * add `backend-dependent` in `uses` Strong preference for open-source solutions (so people can reuse it and learn from code), though it is not a requirement. Though, mention repo and open source license only when it is directly relevant (vs additional materials such as exercises for a book, or Python algorithm). ## Other lists * [Explorable Explanations](http://explorabl.es/) * [Distill](https://distill.pub/) * [Explained Visually](http://setosa.io/ev/) * [AI Experiments with Google](https://experiments.withgoogle.com/collection/ai) ## Inspirations Read [Explorable Explanations](http://worrydream.com/ExplorableExplanations/) by Bret Victor. Inspirations for collecting and displaying content: * [Science-based games - a collaborative list](https://github.com/stared/science-based-games-list) - a list I started (maybe I will turn it int something interactive as well) * [Kaggle Past Solutions](http://ndres.me/kaggle-past-solutions/) - a searchable compilation of Kaggle past solutions * source: [EliotAndres/kaggle-past-solutions](https://github.com/EliotAndres/kaggle-past-solutions) * [D3 Discovery](https://d3-discovery.net/) - finding D3 plugins with ease * source: [https://github.com/wbkd/d3-discovery](https://github.com/wbkd/d3-discovery) ## Design Main layout and styling developed by [Jakub Fogel](https://github.com/fogelkuba) ## TO DO (You are invited to constribute) * Descriptions of sites * Write-up in a different way * Some sorting (alphabetical?) * Share button * Code refactoring :)
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:133291065ca6708a, topic:deep-learning