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Long short-term memory ( LSTM) [1] is a type of recurrent neural network (RNN) aimed at dealing with the vanishing gradient problem [2] present in traditional RNNs. Its relative insensitivity to gap length is its advantage over other RNNs, hidden Markov models and other sequence learning methods.
Sep 2, 2020 · First off, LSTMs are a special kind of RNN (Recurrent Neural Network). In fact, LSTMs are one of the about 2 kinds (at present) of practical, usable RNNs — LSTMs and Gated Recurrent Units...
Jun 5, 2023 · LSTM excels in sequence prediction tasks, capturing long-term dependencies. Ideal for time series, machine translation, and speech recognition due to order dependence. The article provides an in-depth introduction to LSTM, covering the LSTM model, architecture, working principles, and the critical role they play in various applications. What is LST
Jan 4, 2024 · LSTM (Long Short-Term Memory) is a recurrent neural network (RNN) architecture widely used in Deep Learning. It excels at capturing long-term dependencies, making it ideal for sequence prediction tasks.
Jun 10, 2024 · The article provides an in-depth introduction to LSTM, covering the LSTM model, architecture, working principles, and the critical role they play in various applications. What is LSTM? Long Short-Term Memory is an improved version of recurrent neural network designed by Hochreiter & Schmidhuber.
Jul 6, 2021 · Long Short-Term Memory (LSTM) networks are a type of recurrent neural network capable of learning order dependence in sequence prediction problems. This is a behavior required in complex problem domains like machine translation, speech recognition, and more. LSTMs are a complex area of deep learning.
Even Tranformers owe some of their key ideas to architecture design innovations introduced by the LSTM. LSTMs have three types of gates: input gates, forget gates, and output gates that control the flow of information. The hidden layer output of LSTM includes the hidden state and the memory cell internal state.
