# Instructor guide

The learner is expected to know Python arrays/functions and basic supervised evaluation. Use the readiness diagnostic before Lesson 01. If shape reasoning is weak, work on paper before adding training. If chain-rule arithmetic is weak, keep the four-parameter network visible and multiply one path at a time. Calculus notation is introduced locally; no proof-heavy prerequisite is assumed.

Suggested sessions pair reading, hand calculation, prediction-before-run and a small code change. Ask learners to explain why an output should change before they run the program. Do not grade copied reference scores as implementation understanding. Use the public checker for stage mechanics and the rubric for experimental judgment.

Key misconceptions to surface: tensor rank versus matrix rank; nn.Linear stores transposed weights relative to X@W notation; [B] and [B,1] can broadcast incorrectly; half-squared loss differs from MSE; classification losses consume raw logits; zero_grad after backward discards needed gradients; eval is not no_grad; freezing is not eval; last epoch is not always the selected checkpoint; repeated test runs are not more test observations; synthetic perfection is not production validation.

The regularization stress test is intentionally nuanced: the wider network overfits badly by the last epoch, but its best validation checkpoint can still beat the regularized candidate. Teach the observed result instead of promising that dropout/decay always improve performance. Several hyperparameters differ between these two stress candidates, so the result does not isolate the causal effect of one intervention.

Core reference code is readable source. It uses only CPU and local generated data. Before classroom use, verify installation on the actual learner machines, keyboard/browser navigation, diagram readability, screen-reader alternatives and the time required for learners to complete stages. These checks and a beginner pilot have not been performed. Suggested pacing and scoring cutoffs are author choices, not validated learning-outcome measurements.

Assessment privacy: use fresh instructor-held data only if you have a suitable delivery process. The course fixture is public to its recipients and does not provide secrecy. Never describe the code guardrails as preventing a determined learner from accessing test labels. They prevent common accidental workflow mistakes.

Publication readiness requires editorial review, actual website integration, mobile/browser and accessibility QA, installation trials and a learner pilot. This package has not been published. Any live lab integration must explicitly provide PyTorch or clearly label the practice as external/local; a generic browser Python editor is insufficient evidence.