How do I implement dropout in Keras?

How do I implement dropout in Keras?

You can implement dropout in Keras by using the Dropout layer. Just add it to your model like this: model.add(Dropout(0.5)).

What does the dropout rate mean?

The dropout rate is the fraction of the input units to drop. For example, a rate of 0.5 means 50% of the inputs will be randomly set to zero during training.

When should I use dropout in my model?

Dropout is typically used in neural networks to prevent overfitting, especially in deep networks. It's beneficial when you have a large model and limited data.

Can I use dropout with other regularization techniques?

Yes, you can combine dropout with other regularization techniques like L2 regularization to enhance your model's performance.