start with the idea before the implementation.
the mechanisms you need to reason about.
Queries keys values
Queries keys values is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Scaled dot-product attention
Scaled dot-product attention is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Masks
Masks is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Multi-head attention
Multi-head attention is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
turn the lesson into evidence.
implement tiny attention
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
visualize weights
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
test masking
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
prove you can explain and decide.
derive tensor shapes
ask cortex to test me →explain causal masks
ask cortex to test me →separate attention from recurrence
ask cortex to test me →what usually goes wrong.
interpreting weights as explanations
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
quadratic cost
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
mask bugs
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
Create a short Attention engineering note with one working artifact one metric one failure case and one decision about when you would or would not use it.
Save the result in your portfolio or project repository. A strong learning artifact should make your assumptions, metrics and failure analysis visible.