The perceptron is where it all began. Understanding this simple unit—inputs, weights, dot products, and activation functions—gives you the mental model to understand modern neural architectures, from CNNs to Transformers.

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A beginner-friendly introduction to the perceptron, the foundational unit of neural networks. Covers the historical context of Frank Rosenblatt's 1958 Mark I Perceptron, the XOR limitation that triggered the first AI winter, and the mathematical mechanics behind how a perceptron works: inputs, weights, bias, dot products, and step activation functions. Explains how understanding this simple unit provides the mental model for modern architectures like CNNs and Transformers.

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