Long Short Term Memory
Long Short Term Memory (LSTM) is a type of recurrent neural network capable of learning long-term dependencies, making it particularly useful for tasks involving sequences of data. It addresses the vanishing gradient problem often encountered in traditional recurrent networks by selectively remembering or forgetting information through input, output, and forget gates. This allows LSTM to maintain context over extended periods, enabling applications such as natural language processing, speech recognition, and even autonomous driving. As part of the Machine Learning Methods hierarchy, it represents a sophisticated approach for analyzing and modeling complex data patterns in Technology, Artificial Intelligence, and Artificial Intelligence Algorithms.