6.8.1 Deep Learning Projects Roadmap: Train, Inspect, Package

This chapter is the exit point of Chapter 6. A deep learning project is not just a training script. It needs data evidence, shape checks, loss logs, prediction samples, failure cases, and a README.

Look at the Project Loop First

dataset→model→training log→evaluation→failure cases→package

Keep One Evidence Record

Create dl_project_evidence_first_loop.py.

evidence = {
    "task": "image classification",
    "baseline_accuracy": 0.71,
    "current_accuracy": 0.82,
    "failure_case_count": 5,
    "next_step": "inspect confused classes and add augmentation",
}

print("task:", evidence["task"])
print("improvement:", round(evidence["current_accuracy"] - evidence["baseline_accuracy"], 3))
print("failure_case_count:", evidence["failure_case_count"])
print("next_step:", evidence["next_step"])

Expected output:

task: image classification
improvement: 0.11
failure_case_count: 5
next_step: inspect confused classes and add augmentation

This is the project habit: every improvement needs a baseline, metric, failure evidence, and next step.

Evidence to Keep

Package the project like another learner will rerun and review it:

Run Command

exact command that reproduces the result

Dataset Note

where data came from and how it was split

Baseline

first simple score or behavior

Current Result

current metric plus success samples

Failure Cases

at least three wrong or weak examples

Next Step

one change justified by the failures

This keeps the project from becoming a one-time demo. A good Chapter 6 project should be rerunnable, inspectable, and improvable.

Learn in This Order

Order	Read	What to deliver
1	6.8.2 Image Classification	dataset, CNN/transfer baseline, prediction samples
2	6.8.3 Sentiment Analysis	text pipeline, training log, error examples
3	6.8.4 Generative Practice	generated samples and review notes
4	6.8.5 Hands-on DL Workshop	one reproducible PyTorch evidence pack

Project Deliverable Standards

Keep at least these files for one project: README.md, run command, dataset note, model summary, loss curve or log, metric table, prediction samples, failure cases, and next-step plan.

Failure Check

Before calling a project finished, answer:

Baseline

what simple method did this beat?

Metric

what number proves improvement?

Sample Success

which predictions look correct?

Sample Failure

which predictions still fail?

Debug Next

what would you change first, and why?

If you cannot show failures, the project is still a demo, not a learning artifact.

Pass Check

You pass this roadmap when another learner can run your project, inspect the training evidence, see both success and failure samples, and understand what you would improve next.

Check reasoning and explanation

1. A passing answer connects tensors, model layers, loss, backward(), and optimizer updates into one training loop.
2. The evidence should include a runnable mini experiment, tensor-shape checks, and a loss or validation curve you can explain.
3. A good self-check names one failure mode such as shape mismatch, no loss decrease, overfitting, data leakage, or using Attention/Transformer words without explaining the data flow.