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

Revision as of 22:02, 27 April 2026

Information about message (contribute)

This message has no documentation. If you know where or how this message is used, you can help other translators by adding documentation to this message.

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 capturar.

Revision as of 03:35, 27 April 2026 (view source) DeployBot (talk \| contribs) (Batch translate Neural Networks unit 14 → es) Tag: translation ← Older edit		Revision as of 22:02, 27 April 2026 (view source) DeployBot (talk \| contribs) (Batch translate Neural Networks unit 14 → es) Tag: translation Newer edit →
Line 1:		Line 1:
	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 ~~solo~~ perceptrón no puede capturar.		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 a la red aprender relaciones no lineales que un único perceptrón no puede capturar.