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Activation function: Difference between revisions
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{{see also|machine learning terms}} | {{see also|machine learning terms}} | ||
==Introduction== | ==Introduction== | ||
An [[activation function]] in [[machine learning]] is a mathematical function applied to the [[output]] of a [[neuron]] in a [[neural network]]. This determines what happens next based on [[input]] to the neuron and is an essential element of its [[architecture]]. The activation function enables neural networks to learn [[nonlinear]] (complex) relationships between [[ | An [[activation function]] in [[machine learning]] is a mathematical function applied to the [[output]] of a [[neuron]] in a [[neural network]]. This determines what happens next based on [[input]] to the neuron and is an essential element of its [[architecture]]. The activation function enables neural networks to learn [[nonlinear]] (complex) relationships between [[feature]]s and the [[label]]. | ||
==What is an Activation Function?== | ==What is an Activation Function?== | ||