Artificial neural network

What is Artificial Neural Network?
An artificial neural network (ANN) is a computer model based on the structure and functions of biological neural networks. Information flowing through the network affects the structure of the ANN because a neural network makes changes (or, in a sense, learns) based on this input and output.

ANNs are viewed as nonlinear statistical data modeling tools where the complex relationships between inputs and outputs are modeled or patterns are found. ANN is also known as a neural network.
An ANN has several advantages, but one of the most famous of these is the fact that it can actually learn from observing data sets. In this way, ANN is used as a random function approximation tool.

These types of tools help determine the most cost-effective and ideal methods of finding solutions while at the same time defining computational functions or distributions. ANN takes data samples rather than entire data sets to find solutions, which saves both time and money. ANNs are viewed as relatively simple mathematical models for improving existing data analysis technologies.

ANNs have three interconnected layers. The first layer consists of input neurons. These neurons send data to the second layer, which in turn sends the output neurons to the third layer.

The training of an artificial neural network involves the selection of permitted models for which there are several associated algorithms.

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