Layers on the How to Make AI Models track. A dense layer connects every input to every output. Convolution shares weights across space. Recurrent or attention layers handle sequences. Pick the layer that matches the structure you know exists.
This lesson assumes you already worked through Anatomy of a neural net.
The idea in practice
Do not use a giant dense layer on raw pixels if local patterns matter. Do not use convolution on a table of unrelated columns.
A concrete check
goal = {
'track': 'How to Make AI Models',
'lesson': 'Layers',
}
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
Flattening an image and hoping a dense layer invents edges from little data. 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
Choose dense or convolution for three datasets and say why.
Self-check
- Say Layers 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.