Aiconomy

LSTM (Long Short-Term Memory)

A type of recurrent neural network architecture designed to learn long-range dependencies in sequential data, using gated memory cells to selectively remember or forget information.

LSTMs were introduced by Hochreiter and Schmidhuber in 1997 and dominated sequence modeling tasks like speech recognition and machine translation until transformers emerged in 2017. LSTMs solved the vanishing gradient problem that plagued earlier RNNs, enabling learning over sequences of hundreds of steps. Google used LSTMs to improve Google Translate accuracy by 60% in 2016. While transformers have largely replaced LSTMs in NLP, LSTMs remain popular for time-series forecasting and edge deployment due to their lower memory requirements.

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