Phase 0: Vision Foundations · ~75 minutes · Python
Image Classification
A classifier is a function from pixels to a probability distribution over classes. Everything else is plumbing.
Hiring signal: Understanding of image classification internals
What you will learn
- Build an end-to-end image classification pipeline on CIFAR-10: dataset, augmentation, model, training loop, evaluation
- Explain the role of each component (dataloader, loss, optimizer, scheduler, augmentation) and predict how breaking any one of them manifests in the loss curve
- Implement mixup, cutout, and label smoothing from scratch and justify when each is worth adding
- Read a confusion matrix and a per-class precision/recall table to diagnose dataset and model failures beyond aggregate accuracy
Introduction
Type: Build Languages: Python Prerequisites: Phase 2 Lesson 09 (Model Evaluation), Phase 3 Lesson 10 (Mini Framework), Phase 4 Lesson 03 (CNNs) Time: ~75 minutes
Learning Objectives
- Build an end-to-end image classification pipeline on CIFAR-10: dataset, augmentation, model, training loop, evaluation
- Explain the role of each component (dataloader, loss, optimizer, scheduler, augmentation) and predict how breaking any one of them manifests in the loss curve
- Implement mixup, cutout, and label smoothing from scratch and justify when each is worth adding
- Read a confusion matrix and a per-class precision/recall table to diagnose dataset and model failures beyond aggregate accuracy
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.
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