Long Short-Term Memory Networks (LSTM)

Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN) that were introduced by Hochreiter and Schmidhuber in 1997. They were designed to address the problem of vanishing gradients in standard RNNs, which made it difficult for the network to learn long-term dependencies in the input data.

LSTM networks are designed to remember information over long periods of time by using a series of gates to control the flow of information. Each LSTM unit contains a memory cell, which can store information over time, and three gates: an input gate, an output gate, and a forget gate. The input gate controls which information is added to the memory cell, while the forget gate controls which information is removed from the memory cell. The output gate controls which information is output from the LSTM unit.

During training, the LSTM network is updated using backpropagation through time, which involves calculating the gradients of the loss function with respect to the weights of the network. The gradients are then used to update the weights of the network using an optimization algorithm such as stochastic gradient descent.

LSTM networks have been applied to a wide range of applications, including speech recognition, natural language processing, and image captioning. They have also been used as generative models for tasks such as text generation and music composition. One advantage of LSTM networks is their ability to handle variable-length input sequences, which makes them useful for tasks such as language modeling and sequence prediction.

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