Long Short Term Memory Networks
Long Short Term Memory Networks, often abbreviated as LSTM networks, are a type of recurrent neural network architecture designed to effectively capture long-term dependencies in sequential data. They are equipped with special mechanisms called gates that control the flow of information through the network, allowing them to remember important information over extended periods of time while preventing the vanishing or exploding gradient problems commonly encountered in traditional RNNs. LSTM networks have become popular in various applications such as natural language processing, speech recognition, and time series prediction due to their ability to model complex temporal patterns and handle sequences of varying lengths.