Translations:Neural Networks/14/es: Difference between revisions

Latest revision as of 23:40, 27 April 2026

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Message definition (Neural Networks)

where <math>g</math> and <math>f</math> are {{Term|activation function|activation functions}}, <math>\mathbf{W}_1, \mathbf{W}_2</math> are weight matrices, and <math>\mathbf{b}_1, \mathbf{b}_2</math> are bias vectors. The hidden layer enables the network to learn nonlinear relationships that a single perceptron cannot capture.

donde $$ g $$ y $$ f $$ son funciones de activación, $\mathbf{W}_1, \mathbf{W}_2$ son matrices de pesos y $\mathbf{b}_1, \mathbf{b}_2$ son vectores de sesgo. La capa oculta permite a la red aprender relaciones no lineales que un único perceptrón no puede captar.

Revision as of 00:24, 27 April 2026 (view source) DeployBot (talk \| contribs) ([deploy-bot] Translate Neural Networks unit 14 to es) Tag: translation		Latest revision as of 23:40, 27 April 2026 (view source) DeployBot (talk \| contribs) (Batch translate Neural Networks unit 14 → es) Tag: translation
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	donde <math>g</math> y <math>f</math> son funciones de activación, <math>\mathbf{W}_1, \mathbf{W}_2</math> son matrices de pesos y <math>\mathbf{b}_1, \mathbf{b}_2</math> son vectores de sesgo. La capa oculta permite ~~que~~ la red ~~aprenda~~ relaciones no lineales que un perceptrón ~~único~~ no puede ~~capturar~~.		donde <math>g</math> y <math>f</math> son {{Term\|activation function\|funciones de activación}}, <math>\mathbf{W}_1, \mathbf{W}_2</math> son matrices de pesos y <math>\mathbf{b}_1, \mathbf{b}_2</math> son vectores de sesgo. La capa oculta permite a la red aprender relaciones no lineales que un único perceptrón no puede captar.