Loss functions, also known as cost functions, are crucial for the optimization process in machine learning. This post explains the role of loss functions in supervised learning, the differences between training, validation, and testing data, and how loss is calculated and minimized. It also covers various loss functions for regression such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), Logcosh, and Huber loss, providing their mathematical background and practical applications.
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Understanding Loss and Loss FunctionsThe high-level supervised learning processForward passLossBackwards passLoss functionsSummaryReferencesSort: