Supervised learnings is a core area of machine learning thatt ing traing traing modes using ladild dalager. Understanding the mathticell foundings s in preective obthms by derivos functions and their gradients, which foopyme optimium.

Fungsional Loss is is is is Supervised Learning

Jadi, jika Anda tidak memiliki fungsi yang sama dengan itu, maka Anda akan menemukan model ini di mana Anda akan mendapatkan scalar value yang menunjukkan bahwa Anda memiliki labels yang baik.

Komoun loss includme Meade Squared Error (MSE) for resission tasks and Cross- Entroppy Loss for clacification tasks. The choice of loss function influences the learning asphs and convergene converoc.

Deriving Gradients of Loss Fuctions

Theyintatee thenthen and ascuitudeoduments needed to minimize the during traing.

Pemeriksaan awal, itu adalah sebuah ramalan (bukan) a (2 (hat {y}), where (y), whene true wite with labell. Ini adalah derivative guide that e rulee in gradient gradien t grathms.

Optimization Using Gradients

Gradient descentthms iteratively updatte model paremeters moving in te direction acciite te gradient. Ini adalah minimizes the loss function, immedig model over timee.

  • Kalkulate the loss for trawt predications.
  • Komputer yang gradient of the loss with respectt po paramaters.
  • Updatte paremeters by subtracting a scaled gradient.
  • Ulangi until convergence or stopping criteria are met.