Covering all birthdays
Quantifying how likely each birthday is present (covered) in some large group of people.
Pegasus by Satoshi Kamiya, folded by me
Quantifying how likely each birthday is present (covered) in some large group of people.
A transformer for generating text in Julia, trained on Shakespeare’s plays. This model can be used as a Generative Pre-trained Transformer (GPT) with further work. This post was inspired by Andrej Karpathy’s Zero to Hero course.
A radix tree in Julia, built following Test Driven Development (TDD).
Description of the Weiler-Atherton polygon clipping algorithm.
A recent paper caused a stir in the machine learning world. It claimed that a combination of GZip and k-Nearest Neighbours could beat transformers in classification tasks. Here I implement that method in Julia and explore results for two datasets,...
How to calculate the statistical distance between two 2D distributions of points. But first a lesson in bad statistics, the pitfalls of visual solutions and over-complicating a solved problem.
Calculating probabilities for matching games like Dobble.
Classifier-free guidance for denoising diffusion probabilistic model in Julia.
A denoising diffusion probabilistic model for generating numbers based on the MNIST dataset. The underlying machine learning model is a U-Net model, which is a convolutional autoencoder with skip connections. A large part of this post is dedicated to implementing...
Denoising diffusion probabilistic models for AI art generation from first principles in Julia. This is part 1 of a 3 part series on these models.