Visual Lab

Machine learning you can operate. Every lab makes one normally-invisible idea visible, lets you change it, and shows the consequence immediately — including what happens when you push it until it breaks.

6 of 6 labs are ready to run. The rest are planned.

Foundations
1
- Gradient Descent
  How a model actually learns: it measures how wrong it is, works out which way to move each parameter to be less wrong, and takes a small step that way.
  7 stages
Supervised Learning
1
- k-Nearest Neighbors
  This model has no equation and learns nothing at training time. Its decision boundary is an emergent shape, produced entirely by asking which labelled points are nearest.
  3 stages
Unsupervised Learning
1
- K-Means Clustering
  Clustering without labels is a loop of two operations that each improve one thing while holding the other fixed — and it always converges, whether or not it converged to anything sensible.
  3 stages
Neural Networks
1
- Perceptron & Activation Functions
  A neuron is a weighted sum followed by one squashing function — and the slope of that function, not the function itself, is what decides whether learning can happen at all.
  5 stages
Modern ML
1
- Attention
  Attention is not a metaphor. It is a matrix of real numbers saying how much each token reads from each other token, and every row of it sums to exactly one.
  4 stages
Evaluation & RL
1
- Threshold, Confusion Matrix & ROC
  A classifier does not output a class. It outputs a score, and someone chooses where to cut — so accuracy, precision and recall are properties of that choice rather than of the model.
  3 stages