An LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) that is designed to handle long-term dependencies in data. It addresses the vanishing gradient problem of traditional RNNs by introducing a memory cell structure.

  The key component of an LSTM is the memory cell, which allows the network to retain information over long periods. The memory cell consists of three main parts: an input gate, a forget gate, and an output gate.

  1. Input Gate: The input gate governs which values from the current input should be stored in the memory cell. It is responsible for controlling the update of the memory cell.

  2. Forget Gate: The forget gate determines which information should be discarded from the memory cell. It allows the LSTM to remove irrelevant information from previous time steps, preventing it from affecting the current output.

  3. Output Gate: The output gate regulates the information that is output from the memory cell. It decides which values should be passed on to the next time step.

  The LSTM's ability to retain and forget information is what makes it effective in handling long-term dependencies. By selectively updating, forgetting, and outputting information using the gate mechanisms, the LSTM can capture long-range dependencies in sequential data. The gates allow the model to determine which information is relevant and should be remembered or forgotten, enabling it to carry information across long sequences.

  Furthermore, LSTMs can be stacked to form deeper architectures, allowing for even better representation of long-term dependencies. Stacking multiple layers of LSTM cells enables the model to capture more complex patterns and dependencies in the data.

  In summary, an LSTM handles long-term dependencies by utilizing memory cells with input, forget, and output gates. These gates allow the network to selectively store, forget, and output information, enabling it to capture relevant information over long sequences of data.