The 15-Day AI/ML Interview Bootcamp
Day 9 — Neural networks from scratch: perceptron to backpropagation
Build a neural network from a single perceptron up — layers, activations, the forward pass, the chain rule, backpropagation by hand and in code, and stochastic gradient descent — solving XOR from scratch, with the backpropagation questions every machine learning and deep learning interview asks.
You typed "running late, see you at" and the keyboard offered "the office". Nobody wrote that rule. A few million numbers inside the keyboard app got there by being told, a few billion times, exactly how wrong their last guess was — and by passing that blame backward through every single number in the line. Then in an interview someone slid a sheet of paper across the table: "Two-layer network, one hidden unit, one weight. Derive the gradient." Every deep learning round starts there. Today you and I build the thing that keyboard is made of: layers, a forward pass, a loss, and the backward pass…
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