Qeexid
Backpropagation waxay ku dabaqdaa xeerka chain-ka ee calculus-ka isagoo ka bilaabma loss-ka dib ugu socda layer kasta, waxayna hufsan u soo saartaa gradient-yo miisaan kasta.
Haddii aan jirin, tababarka shabakadaha qoto-dheer aad ayuu u adkaan lahaa xagga xisaabinta.
Si fudud
Haddii jawaabta ugu dambaysa khaldan tahay, backprop wuxuu dib u socdaa tallaabo kasta oo xisaabinta isagoo weydiinaya "immisa aad ugu biirsatay qaladkan?"
Halka aad ka aragto
- Tallaabo kasta oo tababar oo PyTorch ama TensorFlow ah waxay isticmaaltaa kala-soocidda toos ah (automatic differentiation), taasoo backprop ku dhex jirto.
- Fine-tuning-ka LLM-yada waxay cusboonaysiisaa miisaannada iyadoo la adeegsanayo gradient-yo backpropagated ah.
Sida ay u shaqeyso
1.Tallaabada hore (forward pass)
Xisaabi saadaalinta iyo loss-ka.
2.Tallaabada dib (backward pass)
Faafi gradient-yada layer layer.
3.Cusboonaysii
Optimizer-ku wuxuu miisaannada u cusboonaysiiyaa iyada oo la adeegsanayo gradient-yaas.
Maxay muhiim u tahay
- Backprop waa sababta barashada qoto-dheer ay u ballaadhi kartay — waxay suurtagal ka dhigtay in shabakadaha layers-ka badan la tababaro.
Inta badan la khaldo
Waa inaad backprop gacanta ku sameeyso.
Framework-yadu si toos ah ayay u xisaabiyaan gradient-yada; fahamka fikradda wali waxtar buu leeyahay marka la baadho tababarka.