Long Short-Term Memory

Long Short-Term Memory (LSTM) networks are a type of recurrent neural network capable of learning long-term dependencies, making them effective for tasks like speech recognition and language modeling. Unlike traditional recurrent networks, LSTMs incorporate memory cells and gating mechanisms that regulate the flow of information, allowing them to selectively remember or forget information over extended sequences, thus mitigating the vanishing gradient problem and improving performance on sequence prediction tasks.