Long Short Term Memory
Long Short Term Memory (LSTM) is a special kind of Recurrent Neural Network (RNN) designed to tackle long-range dependencies in sequences, which are common problems in various applications such as speech recognition, language modeling, and translation. Unlike traditional RNNs that suffer from vanishing or exploding gradient issues when dealing with long sequences, LSTM introduces a memory cell state controlled by input, output, and forget gates, allowing the network to selectively remember or forget information for an extended period. As such, it is particularly well-suited for tasks requiring the understanding of context over time, making it an essential deep learning algorithm in artificial intelligence applications within technology. LSTM can effectively capture long-term dependencies by preserving relevant historical information and discarding irrelevant details, thus providing a robust mechanism to process sequential data with complex temporal dynamics.