In this challenge, I demonstrate the entire process of training and deploying a machine learning classification model in JavaScript – data collection, model training, and prediction!
Challenges
Featured Challenge:
#184 — Elastic Collisions
What happens when two circles collide in a p5.js canvas? In this video, I examine the math and implement idealized elastic collisions in a physics simulation. This video supplements the Nature of Code book series in Chapter 6 which uses 3rd party physics libraries to handle collisions rather than a direct implementation.
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Liven up your video calls with p5.js, ml5.js, Teachable Machine, and OBS Studio!
This challenge uses the pre-trained SketchRNN machine learning model (available with the ml5.js library) to create a p5.js sketch that finishes a person's drawing.
In this challenge, I attempt to create a Ukulele tuner in JavaScript using p5.js, ml5.js and the pre-trained machine learning model CREPE: A Convolutional REpresentation for Pitch Estimation. Can you make one with a more elegant and creative interface?
In this challenge, I attempt to make my own version of Google’s Dinosaur Game (T-Rex run!) with a unique twist – controlling the dinosaur (unicorn, in this case) with a machine learning Speech Commands model!
In this holiday coding challenge, I use ml5.js and Sketch-RNN to generate snowflake drawings.
