Feed-forward neural networks (FFNNs) are composed of 𝑁 stacked layers of 𝑀 neurons. The width of each layer approximates complex functions through the addition of multiple simple linear combinations of the input (each perceptron giving a single linear combination weighted by the activation function). One might note that such a network can only construct linear relationships.