Long Short-Term Memory, or LSTM, is a special type of recurrent neural network designed to remember information for extended periods. Traditional RNNs suffer from the vanishing gradient problem when processing long sequences, but LSTMs solve this using a cell state and three gates that control information flow.
At the core of an LSTM is the cell state, which acts like a conveyor belt running through the entire chain. It carries information across time steps with minimal modifications, allowing data to flow through relatively unchanged and preserving long-term memory.
LSTMs use three main types of gates to regulate information flow. The forget gate decides what information to discard from the cell state. The input gate determines what new information to store. The output gate controls what parts of the cell state to output as the hidden state.
The LSTM information flow process works in three main steps. First, the forget gate removes irrelevant information from the previous cell state. Then, the input gate adds new relevant information to create the updated cell state. Finally, the output gate processes the new cell state to generate the hidden state for the next time step.
To summarize what we have learned about LSTMs: They solve the vanishing gradient problem through a special cell state that preserves long-term memory. Three gates control information flow, enabling the network to learn complex temporal patterns. This makes LSTMs essential for many applications in natural language processing and sequential data analysis.