10.4) MLPs for Regression and Classification

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Chapter 10:

Multilayer Perceptrons (MLPs) can be used for classification and regression tasks by modifying the output layer and activation functions. For classification, MLPs typically use a sigmoid or softmax activation in the output layer, depending on whether it's binary or multi-class, and cross-entropy loss to measure error. For regression, a linear activation function is used in the output layer to predict continuous values, with Mean Squared Error (MSE) as the loss function.

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