Long Short-Term Memory (LSTM) is a specialized type of recurrent neural network (RNN) designed to effectively capture long-term dependencies in sequential data. Traditional RNNs often struggle with the vanishing gradient problem, which hampers their ability to learn from long sequences. LSTMs address this issue by introducing a unique architecture that includes memory cells and gating mechanisms, allowing them to retain information over extended periods.
Key Components of LSTM:
- Memory Cells: Serve as storage units that maintain information over time, enabling the network to remember past inputs.
- Gates: Control the flow of information into and out of the memory cells. The primary gates are:
- Forget Gate: Determines which information from the previous time step should be discarded.
- Input Gate: Decides which new information should be stored in the memory cell.
- Output Gate: Controls which information from the memory cell should be output to the next layer.
These components work together to manage the cell state, allowing LSTMs to maintain and update information as needed.
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