Long Short-Term Memory

Long-Short-Term Memory (LSTM) networks represent a prominent architecture in recurrent neural networks (RNNs). They are particularly renowned for their ability to capture long-range dependencies in sequential data. LSTMs are designed to address the limitations of standard RNNs, primarily their struggles with vanishing and exploding gradient problems during the training process.

At the heart of LSTM networks is a unique memory cell that serves as a dynamic memory unit, engineered to retain information over extended periods. Unlike traditional RNNs, which linearly process data and cannot connect information beyond a few time steps, LSTMs maintain both short-term and long-term memory through a gating mechanism. This mechanism comprises three primary gates: the input gate, the forget gate, and the output gate. Each gate plays a crucial role in regulating the flow of information within the cell.

The input gate determines which information should be added to the memory cell. It considers the current input and the previous hidden state, applying a sigmoid activation function to generate output values between zero and one, essentially deciding the importance of the new input. Conversely, the forget gate assesses the relevance of previous information. It filters the memory cell's current state, effectively discarding data that is no longer useful. Finally, the output gate influences what information is passed to the next hidden state and the overall output of the network after the memory cell's state has been updated.