Friendly machine learning for the web! 🤖
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This project is currently in development.
Friendly machine learning for the web!
ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js.
The library is supported by code examples, tutorials, and sample data sets with an emphasis on ethical computing. Bias in data, stereotypical harms, and responsible crowdsourcing are part of the documentation around data collection and usage.
There are several ways you can use the ml5.js library:
- You can use the latest version (0.6.1) by adding it to the head section of your HTML document:
- If you need to use an earlier version for any reason, you can change the version number. The previous versions of ml5 can be found here. You can use those previous versions by replacing
<version>with the ml5 version of interest:
- You can also reference "latest", but we do not recommend this as your code may break as we update ml5.
You can find a collection of standalone examples in this repository within the
examples/directory. You can also test working hosted of the examples online on the ml5.js examples index website.
These examples are meant to serve as an introduction to the library and machine learning concepts.
Code of Conduct
We believe in a friendly internet and community as much as we do in building friendly machine learning for the web. Please refer to our Code of Conduct for our rules for interacting with ml5 as a developer, contributor, or as a person using the library.
Want to be a contributor 🏗 to the ml5.js library? If yes and you're interested to submit new features, fix bugs, or help develop the ml5.js ecosystem, please go to our CONTRIBUTING documentation to get started.
See CONTRIBUTING 🛠
ml5.js is supported by the time and dedication of open source developers from all over the world. Funding and support is generously provided by a Google Education grant at NYU's ITP/IMA program.
Many thanks BrowserStack for providing testing support.
Thanks goes to these wonderful people (emoji key):
This project follows the all-contributors specification. Contributions of any kind welcome!