How a Perceptron Computes and Learns: The Single-Neuron Linear Classifier

Explain what a perceptron is and how it turns a set of inputs into a decision. ```hint Follow one input through Describe what happens to a single input from the moment it arrives until the perceptron gives its answer, then ask how the numbers it relies on get set. ``` ### Clarifying Questions - Should I cover only the original single-layer perceptron, or also how it relates to the multilayer networks used today? - Would you like the math (the formula and the training rule), or an intuitive explanation I could give to non-technical stakeholders? - Would a small worked example help, such as a simple logic function or a yes/no classification task? ### What a Strong Answer Covers - The computation: inputs, weights, bias and threshold, and the kind of output produced - The geometric picture of the decision boundary a perceptron draws - The learning rule, and the condition under which training is guaranteed to finish - What a single perceptron cannot represent, and how modern networks get around it - A concrete example pitched at the right level for the audience ### Follow-up Questions - Why can't one perceptron reproduce the XOR pattern, and what is the smallest change that fixes it? - How does a perceptron differ from logistic regression, and why did neural networks move from step functions to smooth activations? - When is a simple linear classifier good enough for a product feature, and when would you expect the team to need a deeper network?