How does a network learn?
Start with a single neuron. Discover how small numerical steps become learning.
Build your understanding through explanations, experiments and projects. Follow a course or jump to a topic you want to learn.
Start with a single neuron. Discover how small numerical steps become learning.
Look inside learned features and find the shortcuts that can fool a model.
Follow a word from a numerical representation to a next-token prediction.
Understand the tradeoffs between model size, data, and computation.
Repair an evaluation pipeline and diagnose a training failure, with separate project assessments.
Compare neighbors, fitted trees, numerical regression and class probabilities with simple references before adding complexity.
Learn reconstruction, latent representations and the limits of sampling from small trained models.
Perception workbench: train, stress-test and report — saved evidence and separate objective cases; implementation review pending.
Train a small network, compare validation results, freeze a test evaluation, and write a model card.
Start the classifier projectBrush up on the basics, or jump straight in. We’ll meet you where you are.
Review the basics