Unit ID: DLPY-M01-U05 Estimated active time: 30-40 minutes

Deep learning needs a paper trail

You will create models in this course. You will also create evidence.

Evidence is not decoration. Evidence is what lets you decide whether a model is useful, broken, risky, or not worth continuing.

The five recurring artifacts

You will reuse five artifacts.

1. Experiment log

The experiment log records the question, data, model, settings, results, diagnostics, and next decision.

It stops you from saying:

> I changed a few things and it got better.

Instead, you can say:

> I changed the learning rate from 0.1 to 0.01. Validation loss became stable, but the model still underfit.

2. Dataset card

The dataset card records where the data came from, what it contains, what it misses, and what it must not be used for.

Deep learning can hide dataset problems behind impressive training curves. The dataset card keeps the data visible.

3. Model card

The model card explains the model purpose, training data, evaluation evidence, risks, limits, and release boundary.

For this course, a model card does not approve real use. It documents learning evidence.

4. Diagnostics checklist

The diagnostics checklist helps you debug training problems:

• loss does not fall;
• validation is worse than training;
• predictions are unstable;
• labels are wrong;
• split is leaky;
• model is too simple or too flexible; or
• learning rate is unsuitable.

5. Error-analysis and limitation note

Error analysis asks where the model fails. The limitation note says what the model does not prove.

This is where responsible deep learning becomes practical.

The rule for every module

Every substantial module should answer:

1. What did we try?
2. What evidence did we collect?
3. What failed?
4. What changed?
5. What still must not be claimed?

Practice

Open the learner resources and prepare blank copies of:

• experiment log;
• dataset card;
• model card; and
• error-analysis limitation template.

Fill in only the safe parts for now:

• course name;
• module name;
• your intended learning question;
• no private data; and
• CPU-first runtime.

You will complete the technical sections later.

Mark complete