What is the difference between dropout and L2 regularization?

What is the difference between dropout and L2 regularization?

Dropout is a technique used to prevent overfitting by randomly setting a fraction of input units to zero during training. L2 regularization, on the other hand, adds a penalty equal to the square of the magnitude of coefficients to the loss function, discouraging complex models.

When should I use dropout instead of L2 regularization?

Use dropout when you have a large neural network and want to prevent overfitting by introducing randomness. L2 regularization is more suitable for smaller models or when you want to maintain all features but still control complexity.

Can I use both dropout and L2 regularization together?

Yes, you can use both techniques together. This combination can help improve model performance by leveraging the strengths of each method.

What are the potential downsides of using dropout?

One downside of dropout is that it can slow down the training process since it requires more epochs to converge. Additionally, if the dropout rate is too high, it may lead to underfitting.