👉 RN (Recurrent Neural Network) computing is a specialized type of artificial neural network designed to handle sequential data by maintaining a hidden state that captures information about previous inputs in the sequence. This allows RNNs to process data where the order of elements is crucial, such as time series, natural language, and speech. Unlike traditional feedforward neural networks that treat each input independently, RNNs have connections that form directed cycles, enabling them to retain memory of past inputs and use this context to inform their predictions for future inputs. This feature makes RNNs particularly effective for tasks like language modeling, machine translation, and speech recognition, where understanding the temporal dynamics of data is essential. However, RNNs face challenges with long-term dependencies due to issues like vanishing gradients, which have led to the development of more advanced architectures like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) to better manage these complexities.
