Also known as: Long Short-Term Memory (LSTM) · LSTM · Long Short term Memory
Long Short-Term Memory (LSTM) — архитектура рекуррентной нейронной сети, предназначенная для обработки последовательных данных с сохранением информации на длинных интервалах. Она использует memory cells и gating mechanisms для управления тем, какая информация сохраняется, обновляется или забывается. LSTM помогают уменьшить проблему исчезающего градиента и используются в задачах перевода, распознавания речи, распознавания рукописного текста и моделирования временных рядов.
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.