When should I use dropout in neural networks?

What is dropout in neural networks?

Dropout is a regularization technique used to prevent overfitting by randomly setting a fraction of input units to zero during training.

When should I use dropout?

You should use dropout when you have a large neural network and you're experiencing overfitting, especially with limited training data.

How do I implement dropout in my model?

You can implement dropout by adding a dropout layer in your model architecture, specifying the dropout rate, typically between 0.2 and 0.5.

Are there any downsides to using dropout?

Yes, while dropout helps reduce overfitting, it can also slow down training and may lead to underfitting if used excessively.