What are artificial neural networks? An artificial neural network or its acronym in English (ANN, Artificial Neural Network) is a series of algorithms that look for relationships in a data set. It consists of interconnected nodes that give the appearance of a biological neural network and from which it takes its name (despite the lack of consensus on how it reflects the functioning of the human brain).
Architecture of an artificial neural network
The architecture of these systems is made up of different layers of nodes. The most common structure usually has three layers of nodes interconnected with each other.
The first layer or input layer has input nodes that send data to a second layer. These nodes are passive and simply pass the information to the next layer. The number of nodes in this layer matches the amount of data being sent as an input.
The nodes of the second or hidden layer filter the relevant patterns from those that are not, identifying the important information. These nodes are active, which means that they combine the data coming from the previous layer. Each input that is received is multiplied by a weight and the results are added together and delimited with a function (sigmoid or logistic) to improve efficiency. These nodes usually represent 10% with respect to those of the first layer.
In the third layer, also called the output layer (output layer) the process of the second is repeated and the data is again combined and modified in the active nodes to produce the output values.
Active node of an artificial neural network.
Advantages of artificial neural network
The main advantage of ANN is their ability to complete tasks with infinite combinations. This makes them ideal considering the rising trend of applications based on Big Data.
However, it is its unique ability to make sense of incomplete, ambiguous, or contradictory data that makes it truly valuable. In other words, the ability to use controlled processes when there is no exact model to follow.
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