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Hidden layer: Difference between revisions
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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | ==Introduction== | ||
A [[neural network]] consists of multiple layers: the [[input layer]], where [[data]] is first introduced, and the [[output layer]], where [[prediction]]s are made. Between these layers may exist one or more [[hidden layer]]s which cannot be observed directly by humans and do not form part of either [[input or output [[datasets]]. Instead, hidden layers provide an intermediate representation of input that the neural network can use to make its predictions. | A [[neural network]] consists of multiple layers: the [[input layer]], where [[data]] is first introduced, and the [[output layer]], where [[prediction]]s are made. Between these layers may exist one or more [[hidden layer]]s which cannot be observed directly by humans and do not form part of either [[input]] or [[output]] [[datasets]]. Instead, hidden layers provide an intermediate representation of input that the neural network can use to make its predictions. | ||
Each [[ | Each hidden layer consist of one or more [[neuron]] also known as [[node]]. Each node in hidden layer uses the output from its predecessor as input and applies an [[optimization function]] to it. Usually [[non-linear]], this helps the neural network capture complex patterns in data. The output from these nodes is then fed back into the next hidden layer, with this process continuing until an output layer produces a prediction. | ||
The number of hidden layers and nodes per layer are key [[hyperparameters]] that can significantly influence the performance of a neural network. Too few hidden layers or nodes may prevent your network from learning complex patterns, while too many could lead to [[overfitting]]--wherein one performs well on [[training data]] but poorly on new data. | The number of hidden layers and nodes per layer are key [[hyperparameters]] that can significantly influence the performance of a neural network. Too few hidden layers or nodes may prevent your network from learning complex patterns, while too many could lead to [[overfitting]]--wherein one performs well on [[training data]] but poorly on new data. | ||