Qeexid
Loss function waxay isbarbardhigtaa saadaalinta iyo bartilmaameedka waxayna soo celisaa tiro. Cross-entropy waa mid caan ah kala-soocidda; MSE regresyonka; loss gaar ah ayaa u jira ranking, samaynta (generation), iyo barashada is-barbardhigga (contrastive learning).
Doorashada loss-ku wuxuu qaabeeyaa waxa moodeelku mudnaanta siinayo intii tababarku socdo.
Si fudud
Loss-ku waa dhibcaha xakamiyaha (referee) ee muujinaya intee in le'eg aad ka fogaatay. Tababarku waa in la isku dayo ilaa dhibcaan hoos u dhaco.
Halka aad ka aragto
- Cross-entropy loogu isticmaalo classifier-yada spam-ka.
- Contrastive loss-yada ee moodeelada embedding-ka.
- RLHF wuxuu isticmaalaa moodeelo abaalmarin halkii ay ka isticmaali lahayd loss caadi ah oo la kormeeray.
Sida ay u shaqeyso
1.Saadaali
Moodeelku wuxuu soo saaraa dhibco ama qiyame.
2.Isbarbardhig bartilmaameedka
Formula-da loss-ku wuxuu soo saaraa summad qalad.
3.Backpropagate
Gradient-yadu waxay ka socdaan loss-ka ilaa cusboonaysiinta miisaannada.
Maxay muhiim u tahay
- Loss khaldan → dabeecad khaldan. Isku-dheelitirka loss-ka iyo hawsha waa xirfad aasaasi ah oo ML.
Inta badan la khaldo
Loss yar oo tababar had iyo jeer waxay ka dhigan tahay moodeel wanaagsan.
Waad heli kartaa overfitting; loss-ka xaqiijinta (validation) iyo cabbiraadaha dunida dhabta ah ayaa waxtar badan leh.