a$)$ Perform the backpropagation algorithm step$-$by$-$step for a simple two$-$layer neural network with sigmoid activation function. Derive the update equations for weights and biases.

b$)$ Formalize the universal approximation theorem for neural networks and explain its implications for the expressive power of these models. Discuss the limitations of the theorem and its relationship to the number of neurons and network architecture.