What are the benefits of batch normalization in deep learning?
Batch normalization helps stabilize and accelerate training by normalizing layer inputs, reducing internal covariate shift.
How does it improve the training process?
It allows for higher learning rates and reduces sensitivity to weight initialization, leading to faster convergence.
Are there any downsides to using batch normalization?
Yes, it can add computational overhead and may not work well with certain architectures like RNNs.
Can it be used with other normalization techniques?
Absolutely! Batch normalization can be combined with techniques like layer normalization for improved performance.
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