Long Short-Term Memory

Long Short-Term Memory (LSTM) networks are a type of recurrent neural network architecture designed to effectively learn from sequential data over extended periods. They address the vanishing gradient problem, which is a common issue in training traditional recurrent neural networks, by incorporating memory cells and gating mechanisms. These components enable LSTMs to selectively remember or forget information, making them well-suited for tasks involving long-range dependencies in time series, natural language, and other sequential data.