Why are Convolutional Neural Networks (CNNs) preferred over traditional Neural Networks (NNs) for certain datasets?

Convolutional Neural Networks (CNNs) are preferred over traditional Neural Networks for certain datasets, particularly those involving image and spatial data, because they are specifically designed to recognize patterns and features within those types of data. This capability allows CNNs to outperform traditional Neural Networks, especially on complex tasks such as image classification, object detection, and image segmentation, where recognizing spatial hierarchies and local patterns is crucial.

CNNs utilize convolutional layers that automatically detect features at different levels of abstraction, from edges and textures in lower layers to more complex structures such as shapes and objects in higher layers. This hierarchical learning enables CNNs to generalize better and achieve higher accuracy on challenging datasets, compared to traditional NNs that do not have this structured approach to feature extraction.

In addition to improved performance on difficult datasets, CNNs also incorporate mechanisms like pooling layers that reduce the dimensionality of the input data while preserving important features, further enhancing their effectiveness. This architecture leads to superior results, particularly as datasets grow larger and more complex.