Why are CNNs better at classification than RNNs?

CNNs are better at classification than RNNs for image data because they are designed to handle spatial relationships and patterns. CNNs use convolutional layers to extract hierarchical features, such as edges, textures, and shapes, making them highly effective for image classification. RNNs, on the other hand, are optimized for sequential data, such as text or time-series, as they process data in a temporal manner. CNNs excel in capturing spatial features, while RNNs are better suited for capturing temporal dependencies.