Artificial neural networks (ANNs) have become a key part of modern Artificial Intelligence (AI) and machine learning (ML).
Function approximation in artificial neural networks (ANNs) refers to the ability of the network to learn and model complex functions based on input-output data. ANNs achieve this by adjusting their weights and biases through training, allowing them to approximate non-linear relationships and patterns in the data. This capability makes ANNs suitable for a wide range of tasks, including regression, classification, and more complex problems like image and speech recognition. Ultimately, the goal is to minimize the difference between the predicted outputs and the actual outputs, effectively capturing the underlying function that maps inputs to outputs.
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