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 new information to store in the cell state. Finally, the output gate determines what to output based on the cell state and the current input. This gating mechanism allows LSTMs to overcome the vanishing gradient problem, a common issue in training deep neural networks on sequential data where gradients become too small to effectively update the network's weights.

LSTMs have proven highly effective in a wide range of applications involving sequential data. These include natural language processing tasks such as machine translation, text generation, and sentiment analysis. They are also widely used in speech recognition, time series forecasting, and video analysis. Their ability to capture complex temporal patterns makes them a powerful tool for understanding and modeling sequential information.