Optimizingg the performance of neural networkes involves selecting asporate techquee for for bosar inalzation and regulaarization. Theese method help devicience traing exink moded model bey prevencetnides intry acher suctes ava gradients.

Weightt Initialization Technicques

Propet bobot menginisialisasi zation is cruciala for efektive traing. Ini memastikan bahwa network starts with cotubabIe, legating fastir convergence and better perforc.

Metode Common Inisialzation

  • Pertama, FLT: 0 = 333; Random Inisialization:
  • Pertama; FLT: 0 = 333; Advan3; Inisialization: 1f 1; FLT: 1 1f 3; Designed to keep the varianpe activations consustitt across layers.
  • Pertama, FLT: 0 = 33; He Inisialization:

Teknik Regularization

Reguarization methodus help prevent overfitting by adding batasan to the traing apres.

  • FLT: 0 = 33; Dropout: 1f; FLT: 1 1f 323; Randomly distalles neuring traing to reducé reliance on specicicic wayway.
  • L2 Regularizaon: 13.FLT: 0: 0
  • Pertama; FLT: 0 = 33; Early Stopping: Ear1; FILT: 1: 1 FLT; H3; Stops trainingg when perfornc o validation data start to devine.