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Artificial neural networks (ANNs) are computing systems that are inspired by the biological neural networks that constitute animal brains. These networks are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Artificial neural networks are generally presented as systems of interconnected "neurons" which exchange messages between each other. The connections between neurons can be designed and modified to modify the behavior of the network.
ANNs have been used on a variety of tasks, including pattern recognition, classification, clustering, and prediction. They are commonly used in machine learning and deep learning, which are subsets of AI (artificial intelligence). ANNs have also been used in applications such as medical diagnosis, data mining, and control systems.
ANNs have been used on a variety of tasks, including pattern recognition, classification, clustering, and prediction. They are commonly used in machine learning and deep learning, which are subsets of AI (artificial intelligence). ANNs have also been used in applications such as medical diagnosis, data mining, and control systems.