Apuntes de Redes Neuronales Artificiales
This is an introductory educational resource for newcomers to artificial neural networks, with no novel research contributions.
The handouts introduce artificial neural networks for beginners, covering basic neuron models, learning algorithms like the delta rule and backpropagation, and providing MATLAB/Octave examples for classification tasks.
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.