Neural networks, types and applications

Deep Learning, which is based on the use of neural networks, can be applied to very different types of information, which call for the use of particular networks better suited to achieving specific objectives. The Artificial Neural Networks (ANN), on which Deep Learning is based, are computational models that mimic the functioning of biological neurons. An ANN is made up of nodes (artificial neurons), single processing units that work in parallel, organized in layers or layers: an input layer, multiple hidden layers and an output layer. The nodes "weigh" the input data by categorizing its aspects, and by connecting to other nodes, they transfer them to the next layer until the output is obtained. The weight is the strength of the connection between nodes and represents the influence, positive or negative, of each input on the specific characteristic that must be identified.