https://github.com/stared/interactive-machine-learning-list

A collaborative list of interactive Machine Learning, Deep Learning and Statistics websites
https://github.com/stared/interactive-machine-learning-list

deep-learning explorable-explanations javascript machine-learning statistics vue

Last synced: about 2 months ago
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A collaborative list of interactive Machine Learning, Deep Learning and Statistics websites

• Host: GitHub
• URL: https://github.com/stared/interactive-machine-learning-list
• Owner: stared
• License: mit
• Created: 2018-05-31T12:06:47.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2022-12-09T15:21:34.000Z (almost 2 years ago)
• Last Synced: 2024-05-02T01:02:06.854Z (5 months ago)
• Topics: deep-learning, explorable-explanations, javascript, machine-learning, statistics, vue
• Language: JavaScript
• Homepage: https://p.migdal.pl/interactive-machine-learning-list/
• Size: 4.5 MB
• Stars: 428
• Watchers: 30
• Forks: 42
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

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README

        
# 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 :)