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Convolutional Neural Networks (CNNs) are a subtype of Artificial Neural Networks (ANNs) mostly used for image classification. CNNs follow the biological principle of the replication of a structure capable of identifying patterns to identify these patterns in different locations. It was inspired by the model of cats’ visual system proposed by the Nobel prizes winners Hubel and Wiesel at “Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex”, published in 1962. One of the works that used this inspiration was the Fukushima’s Neocognitron, in 1980, although the word Convolution was not used at the time. Therefore, it is not a coincidence that CNNs are very successful in image recognition. However, they have also shown good results dealing with temporal data such as time series and speech recognition, or even when applied on graphs.
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