Neural network visualizations
Click-step visual explainers built for SML / FS 25–26.
Four self-contained, browser-only visualizations. Each one is a small interactive deck — click next (or use the arrow keys) to advance one step at a time. Light/dark theme toggles in the top-right of every page.
Stack neurons, build curves
Take one neuron, then two, then many: see how a stack of simple ReLU units composes into the curves a small network can actually represent.
Gradient descent
Watch a parameter walk down a loss surface. Step size, momentum, and the high-D loss landscape — without the equations getting in the way.
Convolutional networks: a deep dive
From "a filter is a little picture" through receptive fields, handcrafted vs. learned filters, and what individual neurons want to see — ending with segmentation as the same machinery, per pixel.
U-Net: a deep dive
Big picture → details → big picture. Architecture overview, the three-stage flow through the bottleneck, inside the encoder (conv-blocks + pooling) and decoder (with skip connections), the transposed-convolution mechanic up close, then loss, training, and where the model breaks.