LSTM
LSTM stands for Long Short-Term Memory, which is a type of recurrent neural network (RNN) architecture. LSTM networks are designed to overcome the vanishing gradient problem that occurs in traditional RNNs. This problem arises when the gradient of the loss function becomes too small, causing the weights to update very slowly or not at all. LSTM networks use a memory cell, which is a self-contained module that can store information over time. The memory cell is controlled by three gates: the input gate, the output gate, and the forget gate. These gates regulate the flow of information into and out of the memory cell, allowing the network to selectively remember or forget information. LSTM networks have been successfully applied to a wide range of tasks, including speech recognition, natural language processing, and image captioning.
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