Day 69: CS231n notes: the theory backbone
Consolidating the CV theory
Stanford's CS231n notes are the canonical written backbone for everything this stage covers — convolutions, architectures, detection, segmentation, training dynamics. The roadmap points you here not to learn from scratch (you've built these things) but to solidify the theory and fill gaps the hands-on path skipped. Reading theory *after* implementing hits differently: the notes explain phenomena you've already hit.
- Revisit the convolution and pooling notes — you'll now read them as confirmation, not revelation.
- The training neural networks notes (initialization, batch norm, learning rate) explain the training behaviors you saw on Days 39–40.
- The CNN architectures notes give the lineage (Day 36) in more depth, with the reasoning behind each design.
Read to close gaps, not to restart
You're a senior engineer who has now trained detectors, built a U-Net, and shipped a classifier. Use CS231n surgically — find the topics where your intuition is fuzzy and read those, rather than grinding front to back. Depth where you're weak beats breadth you don't need. This selective-reading discipline is what keeps a 230-day roadmap finishable.
Key terms
- CS231n
- Stanford's Convolutional Neural Networks course notes — a canonical theory reference for computer vision.
- Theory-after-practice
- Reading formal theory after hands-on implementation, so it explains phenomena you have already observed.
Why does the roadmap place CS231n theory reading after you have already built these models, rather than before?
