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updated 5 months ago

Deep Learning Toolbox by Rasmus Berg Palm

Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets and more. With examples. (machine learning, deep learning, autoencoder)

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updated 2 years ago

The matrix implementation of the two-layer Multilayer Perceptron (MLP) neural networks. by Marcelo Fernandes

The matrix implementation of the two-layer Multilayer Perceptron (MLP) neural networks. (ann, mlp, backpropagation)

Y=runMLP(X,Wx,Wy)

[Wx,Wy,MSE]=trainMLP(p,H,m,mu,alpha,X,D,epochMax,MSETarget)

XOR_Example.m

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updated 2 years ago

Multilayer Perceptron Neural Network Model and Backpropagation Algorithm for Simulink by Marcelo Fernandes

Multilayer Perceptron Neural Network Model and Backpropagation Algorithm for Simulink. (ann, mlp, backpropagation)

ANN_MLP_BP_Batch_Mode_Training

ANN_MLP_BP_Incremental_Mode_Training

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updated 2 years ago

Tutorial de Backpropagation - Un algoritmo de entrenamiento para redes neuronales by Paul Acquatella

Tutorial en Español acerca del algoritmo Backpropagation. Para uso académico y educativo solamente. (redes neuronales, perceptron, entrenamiento)

backpropagation.m

forwardcomp(X,W,L)

setupbackpropagation.m

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updated almost 6 years ago

A very simple and intuitive neural network implementation by Carl Löndahl

Short code and easy to understand. Example data set provided. (backpropagation, neural network, machine learning)

mendez()

neural.m

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updated 7 years ago

Control system for DC machine with current back-propagation and two levels of excitation by Sasha Zorin

Control Scheme for DC Machine with back-propagation: robustness modeling, uncertainties detection, c (electronics, backpropagation, control)

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updated 12 years ago

M-files for "Neural Networks" by Herve Abdi

M-files for demos, exercises, and implementations. (neural and fuzzy syst..., autoassociator, heteroassociator)

P=randperm(n)

W=learn(W,F,Y,i,t,theta,eta);

W=learn(W,F,Y,i,t,theta,eta);

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updated 15 years ago

rndmpair.m by Istvan Berkeley

It randomizes the order of vectors within the matrices (fuzzy logic, neural networks, randomize)

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