This project is currently in development.
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 with no other external dependencies.
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.
There are several ways you can use the ml5.js library:
- You can use the latest online version by adding it to the head section of your HTML document:
<script src="https://unpkg.com/ml5@latest/dist/ml5.min.js" type="text/javascript"></script>- Or you can use an specific version of the library: v0.2.1
<script src="https://unpkg.com/[email protected]/dist/ml5.min.js" type="text/javascript"></script>v0.1.3
<script src="https://unpkg.com/[email protected]/dist/ml5.min.js" type="text/javascript"></script>- Or you can download the minified and include the file:
<script src="ml5.min.js" type="text/javascript"></script>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.
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.
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 via Dan Shiffman at NYU's ITP/IMA program.
Many thanks BrowserStack for providing testing support.