Teachable Machine Community
What is Teachable Machine?
Teachable Machine is a web-based tool that makes creating machine learning models fast, easy, and accessible for everyone. You can try it here.
Who is it for?
Educators, artists, students, innovators, makers of all kinds – really, anyone who has an idea they want to explore. No prerequisite machine learning knowledge required.
How does it work?
You train a computer to recognize your images, sounds, and poses without writing any machine learning code. Then, use your model in your own projects, sites, apps, and more.
What is this repository for?
This repository contains two components of Teachable Machine:
-
A libraries section that contains all of the machine learning code used in Teachable Machine. Under the hood we use Tensorflow.js, a library for machine learning in Javascript, to train and run the models you make in your web browser. The
librariessection also contains the API for image, audio, and pose helper libraries that make it easier to use the models exported by Teachable Machine in your own projects. -
A snippets section that contains markdown snippets that are being displayed inside the export panel in Teachable Machine. These snippets contain code and instructions on how to use the exported models from Teachable Machine in languages like Javascript, Java and Python.
How can I send feedback or get in contact with you?
You have a few options:
- Share your projects using #teachablemachine on Twitter or on the Experiments with Google page.
- Open an issue in this repository.
Community Contributions and Projects
- Teachable Machine Node Library for image models (Archived and now continued here)
- Teachable Machine Mobile for image models
Disclaimer
This is an experiment, not an official Google product. We’ll do our best to support and maintain this experiment but your mileage may vary.
We encourage open sourcing projects as a way of learning from each other. Please respect our and other creators’ rights, including copyright and trademark rights when present, when sharing these works and creating derivative work. If you want more info on Google's policy, you can find that here.