Which is NOT a typical application of Recurrent Neural Networks?

Recurrent Neural Networks (RNNs) are particularly well-suited for processing sequences of data due to their ability to maintain a hidden state that captures information about previous inputs. This characteristic makes them ideal for applications where the order and context of information are crucial.

Speech recognition is a common application of RNNs, as it involves processing sequences of audio signals where the temporal dimension significantly impacts understanding. Similarly, language modeling benefits from RNNs because it entails predicting the next word in a sequence based on the preceding words, requiring an understanding of context and sequence.

Time series prediction also leverages RNNs effectively because the data consists of sequences collected over time, where the past observations influence future values. RNNs can learn the underlying patterns and trends from these sequences.

In contrast, image classification typically relies on Convolutional Neural Networks (CNNs), which are designed to process the spatial hierarchies in image data. RNNs are not as effective for this type of task, as images do not have a sequential element like text or time series data. Hence, image classification does not align with the typical applications of RNNs.