Evaluate the impact of an AI application used in the real world. (case study) : Working with X-Ray images: Case Study -

The video tutorial covers image preprocessing techniques, focusing on scaling and rescaling images using min-max normalization instead of dividing by 255. It discusses the impact of rescaling on image quality and the importance of problem-specific considerations, such as detecting anomalies. The tutorial also explains augmentation techniques for training, emphasizing the need for computational resources. Finally, it highlights the application of learned techniques to different datasets, stressing the importance of understanding problem dependencies.

What is the reason for not using the divide by 255 strategy in image preprocessing?

How does reducing the image size to 256 by 256 affect the quality of the image?

In what scenario might reducing image quality still be acceptable?

What is the significance of performing augmentation techniques during model training?

How can prior knowledge from a different dataset be applied to a new dataset in image processing?