Long Short-Term Memory

Long Short-Term Memory (LSTM) networks are a type of recurrent neural network architecture designed to effectively learn and remember long-range dependencies in sequential data. Unlike traditional recurrent networks, LSTMs incorporate memory cells and gating mechanisms that regulate the flow of information, enabling them to overcome the vanishing gradient problem and capture complex temporal patterns. This makes them particularly well-suited for tasks involving natural language processing, speech recognition, and time series analysis, where understanding context over extended periods is crucial.