Anatomy of a neural net on the How to Make AI Models track. A net is layers of weighted sums and nonlinearities. Depth lets it compose features. Width lets each layer represent more patterns. Both cost data and compute.
This lesson assumes you already worked through Ensembles.
The idea in practice
Draw input size, hidden sizes, and output size. Count parameters roughly. If parameters dwarf the number of rows, expect memorization.
A concrete check
goal = {
'track': 'How to Make AI Models',
'lesson': 'Anatomy of a neural net',
}
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
A huge net because a diagram looked impressive. 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
Estimate whether your row count is smaller than your parameter count.
Self-check
- Say Anatomy of a neural net 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.