Fundamentals of Statistics contains material of various lectures and courses of H. Lohninger on statistics, data analysis and here for more.

Multi-layer Perceptron

A multi-layer perceptron consists of units and connections. Each unit has an activation, and each link between two units has a weight. The units are organized in layers. Three different types of units are distinguished:

  • Input units
  • Hidden units
  • Output units

When viewing the neural network as a black box, the hidden units are not visible from the outside. The input units receive the input data, and the output units provide the output.

The calculation of the final output values proceeds layer by layer. First, the input signals are applied to the input layer, and each neuron of the input layer calculates its output value. Next, these values are propagated to the next layer; and so forth, until the output layer is reached. You can experiment with a simple feed-forward network by starting the following  interactive example .