📚 node [[hidden_layer|hidden layer]]
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⥅ related node [[hidden layers]]
⥅ related node [[hidden_layer]]
⥅ node [[hidden-layers]] pulled by Agora
📓 garden/KGBicheno/Artificial Intelligence/Introduction to AI/Week 2 - Introduction/Hidden Layers.md by @KGBicheno

Hidden Layers

Go to [[Week 2 - Introduction]] or back to the [[Main AI Page]] Part of the page on [[Deep Learning]]

Hidden layers are layers of neurons between the input and output layers that allow the neural network to identify features in the input.

But ...

If a network includes too many neurons in a hidden layer, it can overfit and simply memorize the input patterns, which limits the network’s ability to generalize. Too few neurons in the hidden layer can result in the network being unable to represent the input-space features and also limit the networks’ ability to generalize. In general, the smaller the network (fewer neurons and weights), the better the network.

A graphical representation of a hidden layer
⥅ node [[hidden_layer]] pulled by Agora
📓 garden/KGBicheno/Artificial Intelligence/Introduction to AI/Week 3 - Introduction/Definitions/Hidden_Layer.md by @KGBicheno

hidden layer

Go back to the [[AI Glossary]]

A synthetic layer in a neural network between the input layer (that is, the features) and the output layer (the prediction). Hidden layers typically contain an activation function (such as ReLU) for training. A deep neural network contains more than one hidden layer.
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