1. Introduction
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.
2. Networks
One efficient way of solving complex problems is following the lemma “divide and
conquer”. A complex system may be decomposed into simpler elements, in order to be able
to understand it. Also simple elements may be gathered to produce a complex system (Bar
Yam, 1997). Networks are one approach for achieving this. There are a large number of
different types of networks, but they all are characterized by the following components: a set
of nodes, and connections between nodes.
The nodes can be seen as computational units. They receive inputs, and process them
to obtain an output. This processing might be very simple (such as summing the inputs), or
quite complex (a node might contain another network...)
The connections determine the information flow between nodes. They can be
unidirectional, when the information flows only in one sense, and bidirectional, when the
information flows in either sense.
The interactions of nodes though the connections lead to a global behaviour of the
network, which cannot be observed in the elements of the network. This global behaviour is
said to be emergent. This means that the abilities of the network supercede the ones of its
elements, making networks a very powerful tool.

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