When should I use batch normalization in deep learning?

When should I use batch normalization in deep learning?

Batch normalization is typically used in deep learning to stabilize and accelerate training by normalizing layer inputs.

What are the benefits of using batch normalization?

The benefits include faster convergence, reduced sensitivity to initialization, and improved overall model performance.

Are there any downsides to using batch normalization?

Yes, it can add computational overhead and may not work well with small batch sizes or certain types of models.

Can I use batch normalization with recurrent neural networks?

Yes, but it's less common due to the sequential nature of RNNs. Alternatives like layer normalization are often preferred.