#classification
4 articles
01
·CNNs & Image Recognition·★ MEMBER·9 min read
Medical Imaging AI — Validation Design Comes Before the Accuracy Number
An AI that scored beautifully on your own hospital's data turns out to be useless at the hospital next door — a failure that has been reported over and over in medical imaging. Sensitivity and specificity, the way prevalence flips the meaning of a positive result, internal versus external validation, what site-to-site variation actually is, and what regulators are really reviewing: building up, from first principles, the habit of questioning the validation design before the accuracy figure.
02
·CNNs & Image Recognition·★ MEMBER·PAPER·8 min read
The CNN Family Tree — From AlexNet to ResNet and EfficientNet
A decade of CNNs told as two campaigns — the race for depth and the race for efficiency. ReLU, residual connections, and compound scaling explained from scratch with metaphors, interactive figures, and code.
03
·VLMs & Multimodal·★ MEMBER·PAPER·7 min read
Paper Deep Dive — ViT: Treating an Image Like a Sentence
A reading of the ViT paper (Dosovitskiy et al., 2020/2021) grounded strictly in its own text: the move of treating 16x16 patches as words, what the position-embedding ablation actually showed, the price of dropping the convolutional inductive bias, and how conditional the claim 'beats CNNs at scale' really is.
04
·CNNs & Image Recognition·FREE·8 min read
Image Classification from Scratch — The Invention of the Convolution
Feed a photograph straight into a fully connected layer and you get over a hundred million parameters — and a model for which the same cat, moved a few pixels, is an entirely different input. The convolution solves both with one idea: slide a small window. Kernels, stride, padding and pooling, ending with softmax turning scores into probabilities.