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Artificial Neural Networks (ANNs) are computational models inspired by the structure and functioning of biological neural networks, which are the basic building blocks of the human brain. The primary purpose of ANNs is to model complex relationships between inputs and outputs by learning from data. ANNs are a fundamental component of machine learning and are used in a variety of applications, including image and speech recognition, natural language processing, and many other areas of artificial intelligence.

In short, ANNs are:

1. To model mathematical function from inputs to outputs based on the structure and parameters of the networks. ANNs consist of interconnected nodes (neurons) organized into layers. The structure of the network defines how information flows from input nodes through hidden layers to output nodes. Each connection between nodes is associated with a weight, and each node has an associated bias. The combination of weights and biases forms the parameters of the network. The overall structure and these parameters determine the mathematical function the network represents. 

2. To allow for learning the network's parameters based on the data. ANNs are capable of learning from data through a process called training. During training, the network is exposed to a dataset with input-output pairs, and it adjusts its parameters (weights and biases) to minimize the difference between its predicted outputs and the actual outputs in the training data. This process often involves the use of optimization algorithms and a loss function that quantifies the difference between predictions and true values. The objective is to optimize the network's parameters to make accurate predictions on new, unseen data. 

Table 3851. Applications and related concepts of Artificial Neural Networks (ANNs).

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