ml5js / ml5-library

Friendly machine learning for the web! πŸ€–
https://ml5js.org
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deep-learning imagenet javascript lstm machine-learning neural-network p5js p5xjs

Read our ml5.js Code of Conduct and software licence here!

ml5

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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.

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

Please read our Code of Conduct, which establishes our commitment to make ml5.js a friendly and welcoming environment.

Usage

Before getting started with ml5.js, review our Code of Conduct. There are several ways you can use the ml5.js library:

* You can use the latest version (0.12.2) by adding it to the head section of your HTML document: **v0.12.2**

* 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](https://www.npmjs.com/package/ml5). You can use those previous versions by replacing `` with the ml5 version of interest: ```html ``` For example: ```html ``` * You can also reference "latest", but we do not recommend this as your code may break as we update ml5. ```html ``` ## Resources - [Getting Started](https://learn.ml5js.org/) - [API Reference](https://learn.ml5js.org/#/reference/index) - [Examples](https://github.com/ml5js/ml5-library/tree/main/examples) - [Community](https://ml5js.org/community) - [FAQ](https://learn.ml5js.org/#/faq) ## Standalone Examples 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](https://examples.ml5js.org/). 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](https://github.com/ml5js/Code-of-Conduct/) for our rules for interacting with ml5 as a developer, contributor, or as a person using the library. ## Contributing Want to be a **contributor πŸ— 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](CONTRIBUTING.md) documentation to get started. See [CONTRIBUTING](CONTRIBUTING.md) πŸ›  ## 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](https://edu.google.com/giving/?modal_active=none) at NYU's ITP/IMA program. Many thanks [BrowserStack](https://www.browserstack.com/) for providing testing support. ## Contributors Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):

Daniel Shiffman

πŸ’» πŸ’‘ πŸ“† πŸ‘€ ⚠️ πŸ“Ή

CristΓ³bal Valenzuela

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Yining Shi

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Hannah Davis

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Joey Lee

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AshleyJaneLewis

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Ellen Nickles

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Itay Niv

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Nikita Huggins

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Arnab Chakravarty

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Aidan Nelson

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WenheLI

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Darius Kazemi

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Dingsu Wang

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garym140

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Gene Kogan

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Hayley Hwang

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Lisa Jamhoury

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Alejandro Matamala Ortiz

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Maya Man

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Mimi Onuoha

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Yuuno, Hibiki

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Dan Oved

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Stephanie Koltun

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YG Zhang

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Wenqi Li

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Brent Bailey

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Marshal Hayes

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Rob

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Andy Baio

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Wenqi Li

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garym140

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Jim

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Yeswanth

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danilo

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Frederik De Bleser

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Tirta Wening Rachman

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Takanobu Asanuma

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Martin L. Jensen

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Hugo Romano

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Darshan Sen

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Ludwig Stumpp

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Evan Weinberg

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Yong-Yuan Chen

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This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!