LSTM (Long Short-Term Memory)
Long Short-Term Memory (LSTM) is a type of recurrent neural network designed to learn patterns and dependencies in sequential data while retaining important information over longer periods.
What is LSTM?
LSTM networks use memory cells and specialized gates to control what information is stored, updated, and discarded as data moves through the sequence. These mechanisms help address the difficulty traditional recurrent neural networks can have in learning long-term dependencies.
Why is LSTM Important?
LSTMs can capture relationships between events that occur at different points in a sequence, making them useful for tasks where context and order matter. They have been widely used for sequence modeling and remain an important architecture in the development of modern AI systems.
Common use cases
LSTMs are commonly used for time-series forecasting, speech recognition, natural language processing, text generation, anomaly detection, and sequential prediction tasks.