Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN Batch Normalization

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 helps mitigate this by normalizing batches after each layer, which also aids in regularization to prevent overfitting. The decision of when and how to apply batch normalization is a hyperparameter that requires tuning. The tutorial concludes with a preview of implementing batch normalization using the TORCH framework.

What is the relationship between batch normalization and covariate shift?

How does normalizing the batch after every layer help in training?

What are the potential benefits of using batch normalization in mini batch gradient descent?

Why is the choice of when to normalize the batch considered a hyperparameter?

What is the significance of implementing batch normalization using the TORCH framework?