Deep Learning CNN Convolutional Neural Networks with Python - Batch Normalization

The video tutorial discusses the concept of mini batches in gradient descent, highlighting the assumption that all mini batches have the same distribution. It introduces the issue of covariate shift, where this assumption is violated, and presents batch normalization as a solution. Batch normalization standardizes features to have a mean of zero and a standard deviation of one, which helps in resolving covariate shift and provides regularization benefits. The process is applied layer-wise in neural networks, improving convergence speed and addressing gradient problems. The video concludes with a mention of upcoming topics on learning rates.

What are the learnable parameters introduced by batch normalization?

How does batch normalization affect the convergence of gradient descent?

Why is batch normalization considered a default choice when using mini batch gradient descent?