What is the primary purpose of a recurrent neural network (RNN)?

The primary purpose of a recurrent neural network (RNN) is to handle sequential data. RNNs are specially designed to work with sequences of data, making them suitable for tasks where context from previous inputs is crucial. This includes applications such as natural language processing, time series prediction, and speech recognition, where the order of information matters significantly.

Unlike traditional feedforward neural networks, RNNs have connections that loop back on themselves, which allows them to maintain a memory of previous inputs in the sequence. This capability enables RNNs to process and learn from the relationships and dependencies within the data over time, making them an effective tool for any task involving temporal or sequential information.

Other options refer to tasks that are typically handled by different types of neural networks. For example, classifying images is usually the domain of convolutional neural networks (CNNs), while compressing large datasets generally involves techniques tailored for dimensionality reduction. Enhancing audio signals may use a variety of audio processing techniques, but it is not the main utility of RNNs. Thus, the handling of sequential data is the defining feature that underscores the effectiveness of RNNs in their area of application.