Translations:Softmax Function/23/es: Difference between revisions

Latest revision as of 23:41, 27 April 2026

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Message definition (Softmax Function)

This is why binary classifiers typically use a single output neuron with a sigmoid {{Term|activation function|activation}} rather than two neurons with softmax — they are mathematically equivalent.

Por esta razón, los clasificadores binarios suelen usar una sola neurona de salida con activación sigmoide en lugar de dos neuronas con softmax — son matemáticamente equivalentes.

Revision as of 22:04, 27 April 2026 (view source) DeployBot (talk \| contribs) (Batch translate Softmax Function unit 23 → es) Tag: translation ← Older edit		Latest revision as of 23:41, 27 April 2026 (view source) DeployBot (talk \| contribs) (Batch translate Softmax Function unit 23 → es) Tag: translation
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	Por esta razón, los clasificadores binarios suelen ~~utilizar~~ una ~~única~~ neurona de salida con activación sigmoide en lugar de dos neuronas con softmax — son matemáticamente equivalentes.		Por esta razón, los clasificadores binarios suelen usar una sola neurona de salida con {{Term\|activation function\|activación}} sigmoide en lugar de dos neuronas con softmax — son matemáticamente equivalentes.