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Apuntes de Redes Neuronales Artificiales

by   J. C. Cuevas-Tello, et al.

These handouts are designed for people who is just starting involved with the topic artificial neural networks. We show how it works a single artificial neuron (McCulloch & Pitt model), mathematically and graphically. We do explain the delta rule, a learning algorithm to find the neuron weights. We also present some examples in MATLAB/Octave. There are examples for classification task for lineal and non-lineal problems. At the end, we present an artificial neural network, a feed-forward neural network along its learning algorithm backpropagation. ----- Estos apuntes están diseñados para personas que por primera vez se introducen en el tema de las redes neuronales artificiales. Se muestra el funcionamiento básico de una neurona, matemáticamente y gráficamente. Se explica la Regla Delta, algoritmo deaprendizaje para encontrar los pesos de una neurona. También se muestran ejemplos en MATLAB/Octave. Hay ejemplos para problemas de clasificación, para problemas lineales y no-lineales. En la parte final se muestra la arquitectura de red neuronal artificial conocida como backpropagation.


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