Hidden layer
Type of layer in artificial neural networks
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.
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.
In typical machine learning practice, the weights and biases are initialized, then iteratively updated during training via backpropagation.
Sources and credits
This article is adapted from the Wikipedia article “Hidden layer”, written by its contributors and licensed under CC BY-SA 4.0. Fathomly has changed the layout, removed citation markers, navigation and maintenance notices, and adjusted punctuation. This adapted version is shared under the same license. For references, see the original article.
Images, from Wikimedia Commons:
- Example of a deep neural network.png by BrunelloN, CC BY-SA 4.0
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