Long Short Term Memory
Long Short Term Memory (LSTM) is a type of recurrent neural network architecture that is capable of learning long-term dependencies in data. It is designed to overcome the vanishing gradient problem that traditional RNNs face, allowing for more effective modeling of sequential data such as text, speech, and time series. LSTMs use a system of gating units to control the flow of information through the network, enabling it to remember important information for extended periods of time. This makes them particularly well-suited for tasks that require capturing complex patterns and dependencies in data over long sequences.