Module 1 slows the learner down before PyTorch syntax begins.
The main message is:
> Deep learning is a powerful tool choice, not a default upgrade.
Sequence
- Course boundary and PyTorch mechanics focus.
- What deep learning adds through representation learning.
- Why neural networks need data, compute, and careful evidence.
- CPU-first runtime, notebooks, and reproducibility.
- PyTorch setup map.
- Experiment log, dataset card, model card, diagnostics, and limitation habits.
- Readiness diagnostic and knowledge check.
Evidence
Learners produce a readiness diagnostic, setup checklist, and blank evidence templates before moving into tensors.
Production Artifacts
- Runtime decision:
platform/runtime-decision.md - Package plan:
platform/package-plan.md - Pretrained-model policy:
platform/pretrained-model-download-policy.md - Runtime-check notebook:
notebooks/runtime-check-pytorch-cpu.ipynb - Learner resources in
learner-resources/