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.

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.

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.