Mathematical foundations
How Machines Learn
Follow one mistake through vectors, loss, gradients, backpropagation, depth, vision, and generalization.
- Perceptron
- Gradient descent
- Backpropagation
- Convolution
AI Learning Lab
Work through 26 interactive lessons on machine learning, language models, and AI agents. Each lesson pairs a focused explanation with a small experiment you can run in the browser.
The learning loop
Each lesson focuses on one mechanism. You predict the result, run the example, and explain what the evidence shows.
Choose what you think will happen before you run the example.
Change one variable and watch how the mechanism responds.
Compare the hand calculation with the trace shown by the lesson.
Answer a checkpoint and identify the evidence behind your answer.
The curriculum
The three courses build on one another, with the explanation next to the experiment that demonstrates it.
Mathematical foundations
Follow one mistake through vectors, loss, gradients, backpropagation, depth, vision, and generalization.
Inside the transformer
Follow one sentence from text and tokens to contextual states, attention, and the next-token distribution.
Controlled execution
Follow one bounded research goal through context, proposals, tools, policy, evidence, and evaluation.
What the examples show
The core lessons use deterministic browser examples, so you can repeat each calculation. A pinned GPT-2 snapshot adds a real, downloadable model trace; every lesson still labels teaching data and live systems separately.
Exact hand calculation
IncludedVersioned model trace
IncludedLocal AI comparison
Local edition onlyFree public beta
The full 26-lesson curriculum is open while I test the explanations, experiments, and course flow with learners. You do not need an account to start.