An artificial neuron. It has input nodes x0,…,xn{\displaystyle x_{0},\dots ,x_{n}}, weights w0,…,wn{\displaystyle w_{0},\dots ,w_{n}}. The input signals get multiplied with the weights. The results are summed up. Then the activation function φ{\displaystyle \varphi } is applied (e.g. sigmoid, tanh, ReLU, ...). This is then the output o{\displaystyle o} of the artificial neuron. Overall, the perceptron unit is a function f(x0,…,xn)=φ(∑i=0nwixi){\displaystyle f(x_{0},\dots ,x_{n})=\varphi (\sum _{i=0}^{n}w_{i}x_{i})}. Typical choices for activation functions φ{\displaystyle \varphi } are the sigmoid function φ(x)=11+e−x{\displaystyle \varphi (x)={\frac {1}{1+e^{-x}}}} or ReLU φ(x)=max(0,x){\displaystyle \varphi (x)=\max(0,x)}.
The input x0{\displaystyle x_{0}} is called the bias and is constant 1, so x0=1{\displaystyle x_{0}=1}.

  Input-Neuronen
  Hidden-Neuron
Own work
MartinThoma