Millised regulatsioonimeetodid on rakendatavad närvivõrkudele?

Neural network regulation methods include:

• Dropout — randomly disabling neurons during training, which prevents overfitting.
• L1 and L2 regularization — adding a penalty to the loss function for large weights (L1 encourages sparsity, L2 smoothness).
• Early stopping — stopping training when the validation error begins to increase.
• Batch Normalization — normalizing the inputs of each layer, which stabilizes and accelerates training.
• Data augmentation — artificially increasing the training set to improve the model's generalization ability.

These methods help the model to generalize better and avoid overfitting.