Technical review summary

An independent review checked the mathematics, executable examples, data integrity, evaluation boundaries, assessments and static reading package. The reviewed Linux CPU path uses actual PyTorch 2.9.1+cpu.

Passed checks include exact CSV regeneration/roundtrip, split disjointness, training-only preprocessing, independent backprop/finite-difference arithmetic, all loss contracts, sample-weighted aggregation, train/eval/no-grad behavior, restored earlier checkpoints, CNN shape/parameter count and reproducible full-run histories.

The public learner checker initially accepted several incorrect alternatives. It was strengthened to verify exact stage model widths, a positive clamped constant-feature scale, actual returned loss, training mode and numerical inference. All ten intentionally broken/starter variants were rejected after repair; the reference implementation passes.

The capstone selection output was independently unchanged after replacing test features/labels with NaNs. Missing selection, changed plan and repeated final invocation are rejected. These are workflow guardrails rather than secure hidden-test enforcement; the fixture remains visible, and concurrent processes are not a global single-use examination service.

Editorial consistency fixes aligned the lesson's constant-feature rule with the stage contract and clarified that selection constructs held-out tensors but does not evaluate them. The fixed class counts in classification fixtures are distinguished from IID regression sampling.

Static review confirmed all local links/fragments, unique IDs, heading hierarchy, 36 workbook exercises, 18 checkpoint questions and 36 explained answers. Each of the nine SVGs has alternative text and a title/description; learning-curve coordinates match the measured history. Rendered diagrams were visually checked. This is not a browser, assistive-technology or learner-pilot certification. See VALIDATION for the exact remaining gaps.