Learning roadmap
Computer Vision Basics
From pixels to products: how machines see, train a real classifier, know when to use off-the-shelf vision APIs instead, and run vision in production with honest metrics and camera privacy rules.
4 stages~8 h
That leaves 4 stages, ~8 hours of focused work. You can clear it in any order, but the stages compound — try them top-to-bottom your first time.
- You'll pick up:
pixels as numbers, conv filters, edge to object hierarchy, vision transformers, failure gallery
Practice quizzesLab taskBuild a "feature explorer": upload or pick an image, visualize filter activations layer by layer (edges → textures → parts), and a failure-gallery panel with famous misclassifications you can trigger live with small perturbations.
Build it in the LabCheckpointYou can explain to a non-engineer why the model saw a cat in static noise — and what that implies for your use case.
What you'll be able to do
- Explain how a machine "sees" — and why it confuses things any human would never confuse.
- Train and evaluate an image classifier with a confusion matrix you actually read.
- Decide train vs API for a vision task with cost and quality on the table.
- Run vision in production with an edge-case test set and camera privacy rules in place.
Or try a related path