Convolutional Neural Networks (CNNs)

Convolutional Neural Networks are a class of deep neural networks designed to process structured grid data, most commonly images. CNNs automatically learn spatial hierarchies of features — from low-level edges and textures to high-level objects and scenes.

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Why CNNs for Images?

A standard fully-connected (dense) network treats each pixel as an independent input. For a 224x224 RGB image, that means 224 * 224 * 3 = 150,528 input features — and with even one hidden layer of 1,000 neurons, the number of parameters explodes to over 150 million. This is impractical and ignores the spatial structure of images.

CNNs solve this by exploiting three key ideas:

Principle	Description
Local connectivity	Each neuron connects to only a small local region of the input
Parameter sharing	The same filter (kernel) is applied across the entire input
Translation invariance	A feature learned in one part of the image can be detected anywhere

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The Convolution Operation