Also known as: Long Short-Term Memory (LSTM) Β· LSTM Β· Long Short term Memory
Long Short-Term Memory (LSTM) is a recurrent neural network architecture designed to process sequential data while preserving information over long intervals. It uses memory cells and gating mechanisms to control what information is retained, updated, or forgotten. LSTMs help reduce the vanishing gradient problem and are used in tasks such as translation, speech recognition, handwriting recognition, and time-series modeling.
An artificial recurrent neural network architecture used in the field of deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections that make it a "general purpose computer" (that is, it can compute anything that a Turing machine can). It can not only process single data points (such as images), but also entire sequences of data (such as speech or video).
A type of cell in a recurrent neural network used to process sequences of data in applications such as handwriting recognition, machine translation, and image captioning. LSTMs address the vanishing gradient problem that occurs when training RNNs due to long data sequences by maintaining history in an internal memory state based on new input and context from previous cells in the RNN.