computer vision

Convolution and parameter sharing are why a CNN needs a tiny fraction of the weights a dense net would, and why it generalizes across position. The conceptual answer, the feature-hierarchy picture, and the honest take on when a fine-tuned CNN still beats reaching for a giant vision-language model.

Ten X-ray studies a second, a 2-second budget, and a missed cancer is unacceptable. The hard parts are recall-first thresholding, a monitor that watches the radiologist, and surviving a new scanner.

There are millions of landmarks, most with a handful of photos, and the next photo might be of something not in your catalog at all. A flat classifier dies on the long tail and never says 'I don't know.' The interview is embeddings plus retrieval plus a confident refusal.