Back to Home
LSTM
(Long Short-Term Memory)
LSTM is a type of recurrent neural network (RNN) architecture designed to learn long-term dependencies in sequential data. Unlike standard RNNs, LSTMs can maintain information over extended periods through specialized memory cells.
Particularly effective for time-series prediction, speech recognition, and natural language processing, LSTMs address the vanishing gradient problem that plagues traditional RNNs. While newer architectures like transformers (used in GPT models) have surpassed LSTMs in some applications, they remain important for many sequence modeling tasks where computational efficiency is a priority.
Particularly effective for time-series prediction, speech recognition, and natural language processing, LSTMs address the vanishing gradient problem that plagues traditional RNNs. While newer architectures like transformers (used in GPT models) have surpassed LSTMs in some applications, they remain important for many sequence modeling tasks where computational efficiency is a priority.