Temporal Models LSTM

Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN) designed to learn dependencies in sequential data. LSTMs use memory cells and gating mechanisms to selectively retain or forget information, enabling them to learn long-range patterns. They're widely used in time series forecasting, natural language processing, speech recognition, and sequence generation. An LSTM cell contains forget, input, and output gates that control information flow. This architecture solves the vanishing gradient problem that plagued earlier RNNs, allowing LSTMs to learn dependencies spanning hundreds of timesteps.