A small collection of interactive, in-browser explainers I built to help make core ideas in machine learning and AI more intuitive. Each one is a self-contained page you can click through at your own pace — no installation, no code, just open and explore.

Available explainers

  • How a neuron learns — Watch a single neuron learn to detect “is the number big?”, step by step, as it adjusts its weights through training.

  • The deep learning training loop — A tabbed walkthrough of how a deep learning model learns: input, forward pass, predictions, loss function, backpropagation, and the full training loop (after François Chollet’s diagram).

  • From text to vectors — Type a sentence and watch it transform: tokenization, building a vocabulary, and turning tokens into one-hot vectors.

  • From text to embeddings — The full pipeline that turns a sentence step by step into the numbers an AI model can understand, ending in dense embeddings.

  • Perceptron visualiser — A live Streamlit app (running entirely in your browser via stlite) where you set the inputs, weights, bias and activation function of a single neuron and watch how its output changes. Note: the first load takes a few seconds while the Python environment downloads.

  • Transformer architecture explorer — A live Streamlit app (also via stlite) that walks through the components of the Transformer architecture — embeddings, positional encoding, multi-head attention, feed-forward layers and more — with a short quiz to test your understanding. The first load takes a few seconds.

  • The Lego way of building CNNs — Snap together a convolutional neural network like Lego bricks: drag layers — convolution, max-pooling, flatten and softmax — into a stack, then train it right in your browser to recognise handwritten MNIST digits and see how each layer shapes the result.