Artificial neural networks are computational models inspired by an animal’s central nervous system (in particular brain) which is capable of machine learning as well as pattern recognition (Wikipedia).
In these slides, we present the single layer and multilayer perceptrons, which are devoted to supervised learning process. We describe the baseline of the approaches: the difference between the linear (single-layer) and non-linear (multilayer) classifiers; the representation power of the models; the learning algorithm (the Widrow-Hoff rule and the back propagation algorithm).
Keywords: artificial neural network, perceptron, single layer, SLP, multilayer, MLP, widrow-hoff rule, backpropagation algorithm, linear classifier, non linear classifier
Components (Tanagra): MULTILAYER PERCEPTRON
Slides: Single layer and multilayer perceptrons
Tutorials:
Tanagra tutorials, "Configuration of a multilayer perceptron", December 2017.
Tanagra tutorials, "Multilayer perceptron - Software comparison", 2008.
