LSTM

Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture designed for learning and predicting sequences of data with long-term dependencies and time-series patterns. It overcomes the limitations of traditional RNNs by introducing memory cells and gating mechanisms for capturing and retaining information over long time intervals. Readers can explore LSTM's architecture, training techniques, and applications in various domains, such as natural language processing, time series forecasting, and sequence modeling, understanding its capabilities and advantages in modeling sequential data.