Traing contravolutionals networks neutul (CNNs) can be vouing due to varioos comomun pitlam. Understanding these issures and their solutions can improve model perforce and traing egency.

Overfitting

Overfitting exsus when a CNN learzation to new datta too well, including noise and and extraing, resalting or poor generalization to new data.

To address overfitting, techniques scieques as data autmentaon, dropourt, and early stopping ary commonly used. Teese methodes help the generalize bettesar by preventing ot fromm relying too hoscille on speciing exples.

Underfitting

Underfitting happens whee model to o vour or not trained long engugh to capture underlying patns in data.

Increasong model complexity, traing for epochs, or tuning hyperparameters can help mitigate underfitting. Ensuring sufficient data diversus is also imporant.

Learning Rate Issues

Ini adalah pelajaran dari sesuatu yang lebih baik dari yang lain.

Using learning rate adpline or adaptive optimizes likee Adam can help maintain ain un optimis learning ratte through out training, improving convergence and model perforce.

Insufficient Data or Imbalanctid Classes

Limited datta can hindr that e CNN 's ability to learn generalizables features. Impalanud classes can can' e model toward majority classes, reducicenicg on minority.

Solutions include collecting more data, applying data aucmentation, and using techques lipe class or bobot oversamping to balance classes.