Long Short-Term Memory (LSTM)

Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) which is used to model temporal dependencies in data. Specifically, it can “remember” past events or dependences from times previously observed. It is widely used in a number of domains, such as natural language processing, machine translation, image captioning, and speech recognition.

An LSTM is a type of network specifically designed to help a computer remember information for longer than is usually possible with a recurrent neural network. The long short-term memory model works by using “cells” which store information for long periods of time; when a new input is detected, new cells are created that link together with the existing cells. Along with this, the new cells are able to “forget” some of the information stored inside the existing cells, allowing the network to “forget” some of what it has learned.

The key components of the LSTM are the memory cells, forget gates, and input gates. Memory cells are responsible for storing information for long periods of time, and the forget and input gates decide what information should or should not be stored in the cells. These components are what enable LSTM networks to “remember” the past while also being able to take into account new input.

LSTMs are an essential tool in a variety of applications, including natural language processing and image recognition. They are able to take prior knowledge into account and can be used in both supervised and unsupervised learning. Their uses range from predicting stock prices and language translation to autonomous driving and video playback.

Overall, LSTM networks are highly effective neural networks for a variety of applications due to their ability to store information for long periods of time and process new input more efficiently than traditional recurrent neural networks.