LSTMnetværk
LSTMnetværk, or Long Short-Term Memory networks, are a type of recurrent neural network (RNN) specifically designed to handle sequential data and learn long-term dependencies. Unlike traditional RNNs, which struggle to retain information from earlier parts of a sequence, LSTMs possess a sophisticated internal structure that allows them to selectively remember or forget information. This is achieved through the use of "gates": input gates, forget gates, and output gates.
The forget gate determines what information to discard from the cell state. The input gate decides what
LSTMs have proven highly effective in a wide range of applications involving sequential data. These include