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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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machine learning (ML)
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 generate rainbows using the StyleGAN Machine Learning model available in Runway ML and send the rainbows to the browser with p5.js! This challenge is based on the live coding talk from the 2019 Eyeo Festival.
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.
In this multi-part coding challenge, I take a closer look at the Quick, Draw! dataset and create a simple node API to "replay" drawings of rainbows and cats using p5.js.
In this coding challenge, I visualize the outputs of a neural network solving XOR using TensorFlow.js.
In this coding challenge, I use my neural network JavaScript library to create a "color predictor."
In this coding challenge, I use my Toy Neural Networks library to solve the XOR problem.
