Long Short-term Memory

Long short-term memory (LSTM) is a type of recurrent neural network (RNN) that is designed to process sequential data and has the ability to maintain long-term dependencies.

Traditional RNNs have a "memory problem," in which they struggle to remember information from the early stages of a sequence when processing later stages. LSTM networks were introduced in 1997 to address this problem, by using a system of gates that control the flow of information through the network.

LSTM networks have a cell state, which is a "memory" that runs through the entire sequence, allowing the network to remember information over long periods of time. The cell state can be modified or cleared by the gates, which are responsible for determining how much information is allowed to flow through the network at each time step. The gates are controlled by learned parameters, which are updated during training.

LSTM networks have been widely used in a variety of applications, such as language modeling, speech recognition, and machine translation, and have been found to be particularly effective in tasks that require the processing of long-term dependencies in sequential data.