An Artificial Neural Network (ANN) is a computational model that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this model is the structure of the artificial neurons that make up the network. ANNs are used to model complex patterns in data.
There are many different types of ANNs, but they all share a common structure: an input layer, hidden layers, and an output layer. The input layer receives the inputs, while the hidden layers process these inputs and extract features from them. The output layer produces the final results.
The connection between each neuron is represented by a weight. The strength of this connection determines how much influence the neuron has on its neighbors. The weights are typically learned through a process of training the network on data.
ANNs have been used for a variety of tasks, including pattern recognition, image classification, and speech recognition. They are also well suited for modeling nonlinear relationships.
There are a few key disadvantages to using ANNs. First, they can be difficult to train. Second, they can be prone to overfitting, which means that they may perform well on the training data but not generalize well to new data. Finally, ANNs can be computationally intensive, which can make them impractical for some applications.