Long Short-Term Memory

What is Long Short-Term Memory?

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.