Frame the two-layer XOR network experiment

Success is not “I followed the tutorial.” Success is producing evidence that: two-layer net reaches XOR solutions that a single neuron cannot. The accepted input is narrow on purpose: XOR truth table, hidden width, learning rate, epochs. That narrowness is what lets you inspect every field and prevents a toy demo from being narrated as a production system.

Record the baseline you must beat: single-neuron XOR attempt recorded as failing. If the finished artifact cannot beat that baseline on the fixture below, stop and revise the claim before writing more code.

cases=[((0,0),0),((0,1),1),((1,0),1),((1,1),0)]
print('XOR examples',len(cases))

Expected evidence: dead or dimensionally broken network. Treat the printout as a claim about this fixture, not as proof that the toolchain merely started.

For the two-layer XOR network, a decorative win often looks like a clean run that never checks all four XOR cases correct after training; single-neuron baseline fails. Write the metric down now so later pages cannot redefine success after the fact. Also note the operational threat you will eventually gate on: scaling the toy net's accuracy language to production vision claims.

For deep-learning-basics, write the claim on a sticky note in this exact shape: “Given XOR truth table, hidden width, learning rate, epochs, the two-layer XOR network will …”. Fill the ellipsis with the observable part of: two-layer net reaches XOR solutions that a single neuron cannot. Tape the baseline beside it: single-neuron XOR attempt recorded as failing. If someone later replaces your metric with a vibe check, the sticky note is how you push back.

Also sketch the one-sentence user story: a person uses this output to show why a hidden layer is required for XOR while keeping the net tiny. If that sentence needs a dashboard, a model zoo, or five services, the lab scope is too wide—shrink the fixture (XOR four-row table) until the story fits on one screen.

Decide now whether live network calls are allowed on page 1. For this lab they usually are not; inventory and contracts should run offline against XOR four-row table. Note the metric you will eventually require (all four XOR cases correct after training; single-neuron baseline fails) so page 4 cannot invent a softer target. The characteristic failure to keep in mind is vanishing updates from saturated activations, or reporting train accuracy only.

At the experiment brief stage for deep-learning-basics, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about XOR four-row table that later pages inherit without redefining success. Keep that fixture small enough to inspect by hand, keep outputs copy-pasteable as text, and refuse to narrate this baseline as if it were a production SLA: single-neuron XOR attempt recorded as failing.

For this page specifically, success looks like a falsifiable claim and baseline written before coding while still centering the user decision to show why a hidden layer is required for XOR while keeping the net tiny. If you cannot point to a file, command, or assertion that proves that for the two-layer XOR network, stay on this page instead of advancing.

Glossary: deep learning · Glossary: loss function

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