Traditional feature extraction is not required in deep learning because models like CNNs automatically learn features from raw data during training. This capability is one of the main distinctions between deep learning and traditional machine learning.
For instance, a CNN can learn to detect edges, textures, and complex patterns directly from images without manual intervention. This reduces dependency on domain-specific knowledge for feature engineering.
However, preprocessing data to ensure quality, such as resizing images or normalizing pixel values, is still necessary to optimize model performance and convergence during training.