LSTM (Long short-term memory) is a type of recurrent neural network (RNN) used in deep learning to process and predict sequences of data. The LSTM was designed to address the problem of gradient fading in traditional recurrent neural networks, which occurs when error is backpropagated through multiple layers and important information is lost in the process. The LSTM uses a gated cell structure that allows the network to control the amount of information that is stored and forgotten at each time step, making it particularly suitable for processing long-term sequences of data. LSTMs have been successfully used in a wide variety of deep learning applications, such as natural language processing, speech recognition, text generation and time series prediction.
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