Neural networks and their functions

 

Artificial neural networks are a model inspired by the functioning of the human brain. It is formed by a set of nodes known as artificial neurons that are connected and transmit signals to each other. These signals are transmitted from the input to generate an output.

What is the purpose of neural networks :

The main objective of this model is to learn by automatically modifying itself so that it can get to perform complex tasks that could not be done through the classic rule-based programming. In this way you can automate functions that initially could only be performed by people.

How do neural networks work :

As mentioned, the functioning of the networks resembles that of the human brain. Networks receive a series of input values ​​and each of these inputs reaches a node called a neuron. The neurons of the network are in turn grouped into layers that form the neural network. Each of the neurons in the network has a weight, a numerical value, which modifies the input received. The new values ​​obtained leave the neurons and continue their way through the network

Functions of neural networks :

The scope of the functions of the neural networks is very wide, due to their operation, they are able to approximate any existing function with sufficient training. Mainly the neural networks are the modifications for prediction and classification tasks. Their range of action is wide and very useful today, they will not only be used for Industry 4.0 applications (recognition of parts and defects that have not been previously introduced for example), if they are not used children in other areas such as economics , in which they can help predict how much prices will vary over the years, or even in medicine where they are helpful in diagnosing various health problems.

Neural networks have become a key piece for the development of Artificial Intelligence, it is one of the main fields of research and the one that is evolving over time, each time more complex and efficient solutions.