Apuntes de Redes Neuronales Artificiales

TL;DR

This paper provides introductory explanations and MATLAB/Octave examples on artificial neural networks, covering single neurons, the delta rule, and feed-forward networks with backpropagation.

Contribution

It offers a beginner-friendly, comprehensive overview of neural network concepts with practical MATLAB/Octave examples, including the delta rule and backpropagation.

Findings

Demonstrates how a single neuron functions mathematically and graphically.

Provides MATLAB/Octave examples for classification problems.

Introduces the architecture and learning algorithm of feed-forward neural networks.

Abstract

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 est\'an dise\~nados para personas que por primera vez se introducen en el tema de las redes neuronales artificiales. Se muestra el funcionamiento b\'asico de una neurona, matem\'aticamente y gr\'aficamente. Se explica la Regla Delta, algoritmo deaprendizaje para encontrar los pesos de una neurona. Tambi\'en se muestran ejemplos en MATLAB/Octave. Hay ejemplos para problemas de clasificaci\'on, para problemas lineales y no-lineales. En la parte final se muestra la arquitectura de red neuronal artificial conocida como backpropagation.