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Explores the aim and process of batch normalization in deep neural networks, emphasizing its importance in stabilizing mean input and solving the vanishing gradient problem.
Discusses the mean input shift and bias problem in weight updates for neural networks, highlighting the importance of correct initialization to prevent gradient issues.