From Convolution to CNNs

Wed, 11 Nov 2026 21:00:00 +1100

Hand-designed features (colour histograms, SIFT, HOG) followed by a trained classifier was the recipe for image recognition until about 2012. A convolutional neural network learns both halves at once, straight from the pixels. This page builds one from the bottom: why the obvious learned classifiers fail on images, what a convolutional layer does instead, the two formulas that size every layer, pooling and receptive fields, what the filters actually learn, and why very deep networks only train once you give them residual connections. 𐃏

Image-Classification on Aayush Bajaj's Augmenting Infrastructure

From Convolution to CNNs