The video tutorial emphasizes the importance of normalization in image processing, particularly in smoothing the training process. It explains how data points with varying ranges can affect gradients during backpropagation, especially when using activation functions like sigmoid. The tutorial also addresses the issue of outliers and presents different normalization techniques, such as using percentiles and standard deviation. It highlights the need for appropriate normalization methods for different types of images, like natural and X-ray images, to ensure effective training outcomes.

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