Machine learning

Convolution, activation, and pooling in the image classifier used by the demo.

Weighted sums, padding, and the filters used by the convolution demo.

A walkthrough of the calculations in the small model used by the 3D visualization.

Token embeddings, attention, and sampling in the GPT-2 visualization.

Small visual labs for consensus, indexes, embeddings, classifiers, and language-model sampling.

How an image becomes a sequence of patches, and why attention changed the computer-vision toolbox.

Phi-3.5 Vision and Florence-2 look similar at the input, but they are built for rather different jobs.