6.0 Study Guide and Task Sheet: Deep Learning and Transformer Basics
The main study route is now in Chapter 6 entry. Use this page only as a quick checklist while you practice.
One-Line Mental Model
batch data -> model forward -> loss -> backward gradients -> optimizer step -> curves
If the code feels long, find these six steps first.
Practice Checklist
| Check | Evidence |
|---|---|
| I can explain forward, loss, backward, optimizer | training-loop note |
| I can run a minimal PyTorch script | train.py |
| I can print tensor shapes through a model | shape trace |
| I can compare training and validation curves | curve image or CSV |
| I can explain what Attention changes | attention note |
| I can finish the evidence-pack workshop | deep_learning_workshop_run/ |
Evidence Rubric
| Artifact | It should answer |
|---|---|
| Training-loop note | What happens in forward, loss, backward, and optimizer step? |
| Shape trace | How do tensor shapes change through the model? |
| Curve image or CSV | Is the model underfitting, overfitting, or improving steadily? |
| Attention note | What information does attention add, and what remains hard? |
| Failure sample note | Which sample fails, and what does that tell you about data, model, or labels? |
Ready To Continue
Continue to Chapter 7 when you can train one small model, save the training log, inspect failure cases, and explain why the model improved or failed.