Artificial Neural Networks for Beginners

TL;DR

This paper provides a simple, accessible introduction to Artificial Neural Networks (ANNs) and the backpropagation algorithm, aimed at beginners with basic high school knowledge and curiosity about neural networks.

Contribution

It offers an easy-to-understand, non-technical overview of ANNs and backpropagation, facilitating initial learning for newcomers and applications for non-experts.

Findings

Accessible explanation of ANNs and backpropagation

Suitable for beginners with high school education

Includes exercises and online resources for further learning

Abstract

The scope of this teaching package is to make a brief induction to Artificial Neural Networks (ANNs) for people who have no previous knowledge of them. We first make a brief introduction to models of networks, for then describing in general terms ANNs. As an application, we explain the backpropagation algorithm, since it is widely used and many other algorithms are derived from it. The user should know algebra and the handling of functions and vectors. Differential calculus is recommendable, but not necessary. The contents of this package should be understood by people with high school education. It would be useful for people who are just curious about what are ANNs, or for people who want to become familiar with them, so when they study them more fully, they will already have clear notions of ANNs. Also, people who only want to apply the backpropagation algorithm without a detailed and formal explanation of it will find this material useful. This work should not be seen as "Nets for dummies", but of course it is not a treatise. Much of the formality is skipped for the sake of simplicity. Detailed explanations and demonstrations can be found in the referred readings. The included exercises complement the understanding of the theory. The on-line resources are highly recommended for extending this brief induction.