The ml5.js community page is dedicated to highlighting artists, technologists, makers, activists, thinkers, tinkerers, researchers, scientists, designers, students, and anyone/everyone who are working in and around machine learning in thoughtful ways. Many of these posts not only showcase what is possible with ml5.js but also what can be done when applying machine learning methodologies and technologies more broadly. Special emphasis is given to projects that highlight ethical and critical engagement with technology and/or are simply delightful.
This tutorial demonstrates how to create AI image classification using machine learning.
A robotic car with an HM-10 BLE module is controlled by human poses detected in real-time using p5.js and the PoseNet library. A neural network is trained to recognize 5 different poses, and these poses are used to control the car over Bluetooth.
These tutorials demonstrate how to create diverse interactive experiences using p5.js and ml5.js.
This tutorial uses the "pre-trained" MobileNet model to classify the content of an image.
This tutorial shows how ml5 PoseNet (machine learning body pose identification) is combined with p5play (physics and game engine) using p5.js.
This tutorial explores the BodyPose model in ml5.js with MoveNet and BlazePose, showing how to track body keypoints and visualize 'skeleton' connections using live video.
If you are interested to share your work, highlight an interesting article/video, or get in touch, we'd love to hear from you!