Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) designed to handle long-range dependencies in sequential data. Unlike traditional RNNs, LSTMs are equipped with special gates that control the flow of information through the network, allowing them to remember and forget information over long periods.
LSTMs include an input gate, forget gate, and output gate, which regulate the cell state and determine which information to retain or discard. This enables LSTMs to capture temporal patterns and dependencies, making them effective for tasks like language modeling and speech recognition.
LSTMs are widely used in natural language processing (NLP), time series forecasting, and any task where the order of inputs is important, especially when long-term context is needed.