Long Short-Term Memory

Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN) architecture designed to better handle long-range dependencies in sequential data. Unlike traditional RNNs, LSTMs incorporate memory cells and gating mechanisms (input, forget, and output gates) that regulate the flow of information, allowing them to selectively remember or forget information over extended periods. This architecture mitigates the vanishing gradient problem, enabling LSTMs to learn and retain relevant information from earlier time steps, making them highly effective in tasks such as natural language processing, speech recognition, and time series analysis where context and long-term dependencies are crucial.