We'll build a complete multi-layer perceptron (MLP) neural network from the ground up using Rust. Starting with the mathematical foundations of activation functions, we'll progressively construct layers with const generics, wire them into a network, and implement the full backpropagation training algorithm.
Along the way we'll see why a single perceptron can't learn XOR, how hidden layers create nonlinear decision boundaries, and how gradient descent adjusts weights to minimize loss. By the end, our MLP can learn nonlinear functions — something a single-layer perceptron cannot do.
