Which type of neural network is designed to handle sequential data?

Recurrent Neural Networks (RNNs) are specifically designed to handle sequential data, making them particularly effective for tasks such as time series forecasting, natural language processing, and speech recognition. Unlike traditional neural networks that process inputs independently, RNNs utilize feedback loops to maintain a 'memory' of previous inputs. This capability allows RNNs to capture temporal dependencies and patterns in sequences, which is crucial for understanding context in data where the order matters, such as sentences in a text or frames in a video.

The architecture of RNNs involves recurrent connections that allow the network to pass information from one time step to the next. This design enables the model to learn from sequences of varying lengths, adjusting its output based on both current and past data. Consequently, RNNs are a natural choice for applications that involve sequential data, setting them apart from other types of neural networks.

In contrast, Convolutional Neural Networks (CNNs) are tailored for spatial data and are optimal for image-related tasks due to their ability to capture spatial hierarchies through convolutional layers. Deep Learning Networks (DLNs) is a broader term that encompasses various architectures, while Reinforcement Neural Networks refers to a methodology rather than a specific type of network focused on sequences