Are CNNs limited to image data sets only?

Convolutional Neural Networks (CNNs) are a versatile type of neural network originally designed for image data, but their applicability extends far beyond that realm. The architecture of CNNs, which includes layers that apply convolutional filters to extract features from input data, can be adapted to various data forms.

For instance, CNNs are effectively used in tasks involving sequential data, like time-series analysis or natural language processing, where they can capture local patterns in the data. In the case of time-series data, CNNs can identify trends and anomalies by sliding the convolutional filters over time windows. Similarly, they can be applied to audio signal processing, where sound waves can be treated as one-dimensional signals, thus benefiting from the feature extraction capabilities of CNNs.

This adaptability is part of what makes CNNs a powerful tool in many different domains. Therefore, it is accurate to state that CNNs are not limited to image data sets; they can indeed be used for a variety of other data types, which aligns with the chosen answer.