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In artificial neural networks, a hidden layer is a layer of artificial neurons that is neither an input layer nor an output layer. The simplest examples appear in multilayer perceptrons (MLP), as illustrated in the diagram.[1]
An MLP without any hidden layer is essentially just a linear model. With hidden layers and activation functions, however, nonlinearity is introduced into the model.[1]
In typical machine learning practice, the weights and biases are initialized, then iteratively updated during training via backpropagation.[1]
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