A loss function is a mathematical function that quantifies the difference between predicted values and actual values in a model. It plays a crucial role in training algorithms by guiding the optimization process, helping models learn from their mistakes. The choice of loss function can significantly influence model performance, especially in different architectures such as neural networks, where it helps measure how well the model is performing and how to adjust its parameters.
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