What regularization methods of neural networks do you know?

Main methods of regularization for neural networks:

• Dropout — random "turning off" of neurons during training to prevent overfitting.
• L1 and L2 regularization — adding a penalty to the loss function for large weights (L1 promotes sparsity, L2 — smoothing).
• Early stopping — stopping training when the validation set performance ceases to improve.
• Batch Normalization — normalizing layer inputs, which stabilizes and accelerates training.
• Data augmentation — artificially increasing the size of the training dataset.

Example of L2 regularization in Keras:

from tensorflow.keras import layers, models, regularizers

model = models.Sequential([
    layers.Dense(64, activation='relu', kernel_regularizer=regularizers.l2(0.01)),
    layers.Dense(10, activation='softmax')
])

The following methods are used for regularization of neural networks:

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

These methods help the model generalize better and avoid overfitting.