!ml5



_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.

ml5.js is heavily inspired by Processing and p5.js.

Usage

There are several ways you can use the ml5.js library:

* You can use the latest version (0.3.1) by adding it to the head section of your HTML document:

v0.3.1
``javascript

`

* If you need to use an earlier version for any reason, you can change the version number.

v0.3.0




v0.3.0


v0.2.3


v0.1.3




* You can also reference "latest", but we do not recommend this as your code may break as we update ml5.

`javascript

``

Resources

- Getting Started
- API Reference
- Examples
- Community
- FAQ

Standalone Examples

You can find a collection of standalone examples in this repository: github.com/ml5js/ml5-examples

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 user.

Contributing

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 🛠

Acknowledgements

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.

Contributors

Thanks goes to these wonderful people (emoji key):

Daniel Shiffman 💻 💡 📆 👀 ⚠️ 📹	Cristóbal Valenzuela 💻 💡 👀 🔧 ⚠️	Yining Shi 💻 💡 👀 🔧 ⚠️ 🐛	Hannah Davis 💻 💡	Joey Lee 💻 💡 👀 🖋 ⚠️	AshleyJaneLewis 📝 🎨 📋 🖋	Ellen Nickles 📝 🖋 🤔 ✅
Itay Niv 💻 💡	Nikita Huggins 📝 🖋 🤔	Arnab Chakravarty 🖋 📓	Aidan Nelson 💻 💡	WenheLI 💻 💡 🚧 🤔	Darius Kazemi 🤔 💬	Dingsu Wang 💻 💡
garym140 🖋 📝 🤔 📓	Gene Kogan 💻 💡 🤔	Hayley Hwang 💻 💡 🤔	Lisa Jamhoury 💡 🤔	Alejandro Matamala Ortiz 🎨 🖋 📝	Maya Man 💻 💡	Mimi Onuoha 🤔 🖋 👀
Yuuno, Hibiki 💻 💡 🚧	Dan Oved 💻 💡 💬 🤔	Stephanie Koltun 💻 💡 🖋 📝 🎨	YG Zhang 💻 💡 🤔	Wenqi Li 💻 💡 🚇	Brent Bailey ⚠️ 💻 💡	Jonarod 💻
Jasmine Otto 💻 ⚠️ 💡	Zaid Alyafeai 💻 💡 🤔 💬	Jacob Foster 💻 💡 ⚠️	Memo Akten 💻 💡	Mohamed Amine 💻 💡 🤔 ⚠️	Oliver Wright 💻 ⚠️	Marshal Hayes 📖
Reiichiro Nakano 💻 ⚠️ 💡	Nikhil Thorat 💻 💡 🤔 🚇	Irene Alvarado 💻 💡 🚧 🤔	Andrew Lee 💻 💡 🤔	Jerhone 📖	achimkoh 💻 💡 ⚠️	Jim 💡 📖 🖋
Junya Ishihara 🚧 💻	Naoto HIÉDA 🚧	aarón montoya-moraga 🚧 💡	b2renger 💻 🚇	Aditya Sharma 🚧	okuna291 🤔	Jenna 🤔
nicoleflloyd 🖋 🎨 📓	jepster-dk 💻 🤔	Xander Jake de los Santos 🤔	Cassie Tarakajian 🐛 🚇 🤔	Dave Briccetti 🐛	Sblob1 🐛	Jared Wilber 💡 🤔 💻
danilo 💻 🤔	Emma Goodliffe 🤔

This project follows the all-contributors specification. Contributions of any kind welcome!