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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?== | ||
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===Softmax=== | ===Softmax=== | ||
The [[softmax]] function is a popular activation function used in the [[output layer]] of neural networks for [[multi-class classification]] problems. This activation function maps input into an interval probability distribution over output [[ | The [[softmax]] function is a popular activation function used in the [[output layer]] of neural networks for [[multi-class classification]] problems. This activation function maps input into an interval probability distribution over output [[class]]es. | ||
== | ==Explain Like I'm 5 (ELI5)== | ||
Activation functions are like special glasses that help computers see better. They alter pictures, sounds, or other items by altering their hue or brightness; this makes it easier for the computer to differentiate what the picture or sound is and how best to process it. Different glasses are used for various tasks like seeing colors or finding loudest noise. | Activation functions are like special glasses that help computers see better. They alter pictures, sounds, or other items by altering their hue or brightness; this makes it easier for the computer to differentiate what the picture or sound is and how best to process it. Different glasses are used for various tasks like seeing colors or finding loudest noise. | ||
[[Category:Terms]] [[Category:Machine learning terms]] | [[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]] | ||