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.
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: the Core ML: Algorithms from Scratch course, the Deep Learning from Scratch course, Model Evaluation
- Time: ~75 minutes
Objective
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, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.
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