Hog
2 long-form posts on Hog: machine-learning research by Taha Bouhsine, each built around live, in-browser interactive visualizations.
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The Hand-Built Network, in JAX/Flax NNX
A runnable companion: build the training-free image classifier from the post in JAX. The feature extractor is pure JAX (Sobel gradients, orientation binning, patch pooling); the classifier is a Flax NNX module holding k-means prototypes that votes with the Yat kernel. Nothing is trained, and it reproduces the 83.3% on Fashion-MNIST.
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You Don't Even Have to Train the Features
The last post trained a backbone and built the classifier by hand. This one builds the features by hand too: oriented-edge and corner detectors pooled over a grid of patches, the way computer vision worked for decades. Feed those to the same constructed Yat head and, with nothing trained anywhere, it matches the trained backbone on Fashion-MNIST point for point, within a couple of points of a fully trained network. The whole network is hand-built and readable end to end.