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

The video tutorial discusses data augmentation, a technique used to enhance datasets by creating variations of existing data. It is particularly useful when dealing with insufficient data or outliers. Techniques such as flipping, rotation, cropping, and adding noise are employed to increase dataset size and improve model accuracy. The tutorial also explains the importance of data augmentation in reducing overfitting and handling lower quality data. Two types of transformations, linear and offline, are introduced, with examples illustrating their application in different contexts.

How can data augmentation reduce overfitting in machine learning models?

What role does the quality of training data play in the effectiveness of data augmentation?

What is the difference between linear transformation and offline transformation in data augmentation?