A Recurrent Neural Network (RNN) is a class of artificial neural networks designed for processing sequential data by maintaining a form of memory. Unlike traditional feedforward neural networks, RNNs have connections that loop back on themselves, allowing information to persist and be utilized across time steps. This makes RNNs particularly suited for tasks involving time series data, natural language processing, and other applications where context and sequence are crucial. Variants such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks have been developed to address issues like vanishing gradients, enabling RNNs to learn long-range dependencies more effectively.