Long Short-Term Memory
(LSTM) networks are a special kind of recurrent neural network (RNN) capable of learning long-term dependencies. They are designed to avoid the long-term dependency problem, which is the challenge of learning to remember information for long periods. LSTMs are well-suited to classifying, processing, and making predictions based on time series data, since there can be lags of unknown duration between important events in a time series. LSTMs work well on a large variety of problems and are now widely used in the industry, from speech recognition to language modeling and even in the realm of finance for stock market predictions.
A type of recurrent neural network (RNN) architecture designed to remember information for long periods.
Examples
Google Translate: Uses LSTM networks to understand and translate text from one language to another, maintaining the context of sentences even when they are long.
Speech Recognition: Apple's Siri and Amazon's Alexa utilize LSTM networks to process and understand spoken language, enabling them to respond accurately to user commands.
Additional Information
LSTM networks contain special units called memory cells that can maintain information in memory for long periods.
They use gates (input, forget, and output gates) to regulate the flow of information, ensuring that relevant information is remembered and irrelevant information is forgotten.