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]
References
edit- ^ a b c Zhang, Aston; Lipton, Zachary; Li, Mu; Smola, Alexander J. (2024). "5.1. Multilayer Perceptrons". Dive into deep learning. Cambridge New York Port Melbourne New Delhi Singapore: Cambridge University Press. ISBN 978-1-009-38943-3.