In a neural network with a single hidden layer, the computation begins by calculating an intermediate variable for the hidden layer. Assuming the input example is represented by a vector $\mathbf{x} \in \mathbb{R}^d$ and no bias term is included, the intermediate variable $\mathbf{z} \in \mathbb{R}^h$ is obtained by multiplying the hidden layer's weight parameter matrix $\mathbf{W}^{(1)} \in \mathbb{R}^{h imes d}$ by the input vector:

$\mathbf{z} = \mathbf{W}^{(1)} \mathbf{x}$