In which scenario would a recurrent neural network (RNN) be less appropriate than a convolutional neural network (CNN)?

The scenario where a convolutional neural network (CNN) is more appropriate than a recurrent neural network (RNN) is when manipulating images. CNNs are specifically designed to process data with a grid-like topology, such as images, due to their ability to capture spatial hierarchies through the use of convolutional layers. These layers enable the network to identify patterns and features (like edges, textures, and shapes) within the visual data efficiently.

In contrast, RNNs are optimized for sequential data and are particularly effective in tasks that involve time series data, natural language processing, or sequence prediction, where temporal relationships or order are significant. While RNNs can be used in image processing for specific tasks, they are generally less efficient than CNNs for images, as they do not leverage the spatial structure of images and can entail more computational complexity.

Thus, choosing CNNs over RNNs for image manipulation is based on their architectural advantages in handling the inherent properties of images.