Multilayer Perceptron

To create more complex models, we can combine multiple perceptrons, all with their own set of weights and biases.

The outputs of a layer can be calculated with this formula, where the input values get a weight with $W$, a bias value $b$ is added and the Sigmoid Activation Function will be applied to the resulting value. $y=\sigma (W\cdot x+b)$ By combining multiple layers, using the outputs of former layers as new inputs, the following recursive formula applies: $y^{i+1}=\sigma (W^i\cdot y^i+b^i)$