#cnn
7 articles
01
·CNNs & Image Recognition·FREE·PAPER·9 min read
The ImageNet Moment — The Day Deep Learning Won
In 2012 an image-recognition contest saw its error rate fall from around 26% to 15% in a single year. Neural networks had existed for three decades — so why then? This is the story of the moment data, compute, and method finally lined up, told down to the technical details.
02
·Linear Algebra·★ MEMBER·PAPER·10 min read
Symmetry and Equivariance — How Group Theory Shapes Network Design
How to bake "a shifted cat is still a cat" into the architecture itself — from the four axioms of a group, through the definition of equivariance, to why convolution is the only translation-equivariant linear map and how AlphaFold handles 3D geometry. Including the cases where imposing symmetry backfires.
03
·CNNs & Image Recognition·★ MEMBER·PAPER·9 min read
Object Detection from Scratch (from YOLO to DETR)
A from-zero guide to object detection: the classic toolkit of two-stage detectors, anchors, and NMS — and how DETR reframed the whole problem to make all of it unnecessary. Primary source: the DETR paper.
04
·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.
05
·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.
06
·CNNs & Image Recognition·★ MEMBER·11 min read
Anomaly Detection from Scratch — Learning From Normal Alone
Why an AI can learn from good units alone on a factory floor where defect samples barely exist. Two rulers — reconstruction error and density estimation — built up from metaphor to equations, all the way to the part that decides everything in practice: choosing the threshold.
07
·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.