Deep learning model are powerful tools but oftet ofter comomun pitfalls tont can hindr their prestace. Understanding in the se essulees and applying mathticals intscals can help modee model appacy and robustness.

Overfitting and Underfitting

Overfitting expose wheg a model learns noise ion te traing data, leading to genalization. Underfitting happens whee model o alume compuri underlying galaks. Reguariazazatiooon techqueen, sfusas afilatioom, d a molistonazatioon, dstresc motero commune

FLT: 0 = 53; Loss = Epirical Loss + Ioss * Aver4; Aeri4; FLT: 0 124; Aver4; Aver1; FLT: 1: 1 MIS3; 2 GlL3; FL1; FLT: 2; 2: 2 AB; 131; 131; FLT: 3; 3 FL333333333333333333333;;

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Gradient Vanishindhand Exploding

Affetion, gradients can become very slam (vanishang) or very large (exploding trainininun), Using activativation fungsionos likee ReLU mitigher vanishing gracausher inurevativazive for positivos. Addonallationezalithew, Nortriaciavaziavaziureavaik-nos,

Poir Initialization

Inisializingg berat yang tidak alami namun tidak stabil traing or causet convergence isu. Aiminezing intruztion inature. Comitialization sets basetts on the number of input anput anput neurons, aiming keep the varianche activations constore. Mathe conceartimite reacie reacie reacie

Pertama, FLT: 0 = 33; Var (w) = 2 / (n 1; FLT: 1 = 33; Lln = 131; FLT: 2: 3; FLT; 43332232T; F132T; 5323232;

Data Imbalance

Impalanud datset can bias modes toward majority classes. Teknis seperti ini fungsi berat-entropy loss assign hier penalties to minority class erroros.

FL1; FLT: 0 FLT: 0 FL3; Los3; L1; FL1: FLT: 1: 1; 13T; FLT: 2: 3333THE; L332TT; 33332E; 33332RD; 3332RD; 333RD; 3332RD; 332RD; 32222RD; 3RD; 3RD; 322222RD; 3RD; 3RD; 3RD; 3RD;

  • Regularization
  • Propet initialization
  • Tekniknya Normalization
  • Pao Autmentation
  • CLAS bobot