Attention on the How to Make AI Models track. Attention lets a position look at other positions and take a weighted sum of their vectors. The weights are computed from the data, not fixed in advance.
This lesson assumes you already worked through Sequence models.
The idea in practice
You can explain a prediction by the positions with high weight, but weights are not a full explanation. Use them as a clue.
A concrete check
goal = {
'track': 'How to Make AI Models',
'lesson': 'Attention',
}
checks = [
'input available at decision time',
'score matches the real decision',
'failure case written down',
]
print(goal['lesson'])
for item in checks:
print('-', item)
Run the sketch locally if you have Python. The printout is a reminder of the checks, not a trained model. Replace the strings with the real inputs from your own example before you treat it as a design.
What usually goes wrong
Treating an attention map as legal proof of why a decision was made. When this happens, stop adding parameters or tools. Fix the check, the data, or the permission, then run the same example again.
What to write down
- The input you are allowed to use at decision time.
- The output and the score or pass rule.
- One failure you will test on purpose.
- What you will not claim the system can do.
Practice
Describe attention as a weighted sum in one sentence.
Self-check
- Say Attention in one sentence that mentions an input and an output.
- Name the failure mode in this lesson and the check that would catch it.
Done when: you can explain this lesson without the page open, and you have a written failure case.