Set release boundaries for the two-layer XOR network

Operational risks for the two-layer XOR network center on scaling the toy net's accuracy language to production vision claims, plus the earlier failure mode (vanishing updates from saturated activations, or reporting train accuracy only). Safety lives in executable gates, allowlists, redaction, and a named owner—not in a warning paragraph under an unsafe function.

x=[1.0,0.0]
assert all(0<=v<=1 for v in x)
max_weight=1.0
assert max_weight<=5
print('input and weight bounds passed')

Expected evidence: stage evidence for security ops. A failed assertion means stop, investigate, and do not publish the two-layer XOR network.

Name who can disable the feature, what data is retained, and how to roll back to the last known good artifact. Pin the reviewed configuration (versions, thresholds, allowlists) so “what shipped” is reconstructable for deep-learning-basics.

Write the release blocker as a predicate, not a feeling: “Do not ship the two-layer XOR network if scaling the toy net's accuracy language to production vision claims.” Pair it with a passing control that shows the reviewed configuration still works for XOR four-row table. Name an owner and a rollback handle (git tag, docs_version, previous image).

Security pages must not paste the happy-path demo. If your gate code looks like the implementation page, replace it with a deny/allow check aimed at scaling the toy net's accuracy language to production vision claims.

State the data retention rule in one line (what is stored, for how long, who can read it). Then state the kill switch (env flag, config pin, or feature owner). The two-layer XOR network is not shippable without both, even when all four XOR cases correct after training; single-neuron baseline fails looks healthy.

At the safety and operations 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 an executable deny gate for the lab-specific threat 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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