Machine Learning: Multi Layer Perceptrons

Machine Learning: Multi Layer PerceptronsProf. Dr. Martin RiedmillerAlbert-Ludwigs-University FreiburgAG Maschinelles LernenMachine learning : Multi Layer Perceptrons Multi Layer Perceptrons (MLP) learning MLPs function minimization: gradient descend & related methodsMachine learning : Multi Layer Perceptrons networks single neurons are not able to solve complex tasks ( restricted to linearcalculations) creating networks by hand is too expensive; we want to learn from data nonlinear features also have to be generated by hand; tessalations becomeintractable for larger dimensionsMachine learning : Multi Layer Perceptrons networks single neurons are not able to solve complex tasks ( restricted to linearcalculations) creating networks by hand is too expensive; we want to learn from data nonlinear features also have to be generated by hand; tessalations becomeintractable for larger dimensions we want to have a generic model that can adapt to some training data basic idea: Multi Layer perceptron(Werbos 1974, Rumelhart, McClelland, Hinton1986), also namedfeed forward networksMachine learning : Multi Layer Perceptrons in a Multi Layer perceptron standard Perceptrons calculate adiscontinuous function:~x7 fstep(w0+h~w, ~xi) 8 Machine learning : Multi Layer Perceptrons in a Multi Layer perceptron standard Perceptrons calculate adiscontinuous function:~x7 fstep(w0+h~w, ~xi) due to technical rea

Outline multi layer perceptrons (MLP) learning MLPs function minimization: gradient descend & related methods Machine Learning: Multi Layer Perceptrons – p.2/61

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