RNN (Recurrent Neural Network)

An RNN (Recurrent Neural Network) is a type of neural network designed to handle sequential data — such as time series and natural language — that has an inherent order. Unlike standard neural networks, which process inputs and outputs on a one-to-one basis, RNNs maintain an internal state (the previous output) as they process sequences step by step. This ability to propagate information across time makes RNNs well-suited for tasks such as: • Understanding context in text (natural language processing) • Processing continuous streams of data such as audio and music • Sensor data analysis and prediction • Time-series anomaly detection and classification The core mechanism of an RNN is a loop structure where each time step's output feeds back into the input for the next step — this loop acts as the model's "memory." However, standard RNNs struggle to learn long-range dependencies due to the vanishing gradient problem, which limits their performance on long sequences. To address this, improved architectures such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) were developed, enabling more complex sequential processing. While Transformer-based models (BERT, GPT, etc.) have become the dominant approach, RNNs remain in use for lightweight devices and real-time processing tasks, thanks to their simpler structure and lower computational cost.