The video tutorial discusses batch normalization in the context of mini-batch gradient descent. It highlights the issue of covariate shift, where the training and test set distributions differ significantly. Batch normalization, applied after every layer or selectively, can mitigate this problem and also provide regularization to prevent overfitting. The decision of when to apply batch normalization is a hyperparameter that requires tuning. The tutorial concludes with a mention of implementing batch normalization using the TORCH framework.

Why is it important to determine the layer after which to apply batch normalization?

What role does the choice of framework play in implementing batch normalization?