Neural Network Foundations

Define the mathematical structure of an artificial neuron and a multi-layer perceptron (MLP).

Compute the output of an MLP for a given input vector using matrix operations (forward pass).

Define a loss function (e.g., cross-entropy) and explain its role in quantifying model prediction error.

Describe how backpropagation and gradient descent are used together to update network parameters and minimize loss.