Source register and attribution

Checked 2026-10-14. Text, exercises, code, diagrams and data are original teaching work; external documentation is linked rather than copied. Examples are not adapted from proprietary course material. Documentation supports API contracts; measured outputs come from this package's executed programs. Timings and grading standards are author proposals, not external research findings.

Version policy: the verified baseline is PyTorch 2.9.1+cpu and Python 3.12.14. Versioned 2.9 API pages avoid confusing a moving latest-docs build with the tested runtime. The official previous-versions page lists this stable release and CPU installation route. No claim that 2.9.1 is the latest release is made.

Primary references by teaching purpose

• Installation and exact stable CPU release: PyTorch previous versions
• Tensor constructor/dtypes: torch.tensor, Lesson 01
• Stored linear weight dimensions: nn.Linear, Lessons 01/06
• Broadcasting semantics: broadcasting notes, Lessons 01/08
• Rectified activation: nn.ReLU, Lessons 02/03
• Regression reduction: MSELoss, Lesson 02
• Binary raw-logit contract: BCEWithLogitsLoss, Lesson 02
• Multiclass indices/raw logits: CrossEntropyLoss, Lessons 02/09
• Reverse-mode derivative API: Tensor.backward, Lesson 03
• Finite-difference checking guidance: gradcheck, Lesson 03
• Dataset and loader contracts: data loading, Lesson 04
• Standard-deviation correction: torch.std, Lesson 04
• Leakage and train-only fitting: scikit-learn common pitfalls, Lessons 04/06/12
• Basic optimization step: SGD, Lessons 03/05/08
• Gradient clearing: Optimizer.zero_grad, Lessons 05/08
• Mode/state/parameter APIs: nn.Module, Lessons 05/10/11
• Suppressing autograd recording: no_grad, Lesson 05
• Composing modules: Sequential, Lesson 06
• Decoupled weight decay implementation: AdamW, Lesson 07
• Dropout training/evaluation behavior: Dropout, Lesson 07
• Cross-correlation and image shapes: Conv2d, Lesson 09
• Determinism boundaries and seeds: reproducibility notes, Lesson 10
• Feature extractor versus fine-tuning framing: official transfer-learning tutorial, Lesson 11. The course does not execute its downloaded-model path.
• Original transformer architecture reference: Vaswani et al., Attention Is All You Need, Lesson 11. Our two-key arithmetic illustrates one attention operation, not the paper's full trained system.

Original artifacts and limits

The scalar chain-rule example, synthetic generators, all nine SVG visuals, diagnostic cases, assessment prompts and capstone are original. No external images, model weights or datasets are redistributed. Diagrams have descriptive text and SVG title/description fields, but assistive-technology behavior has not been certified. The independent review and validation record separate actual execution evidence from proposed classroom/platform behavior.