LSTM, or Long Short-Term Memory, is a type of artificial neural network designed to recognize patterns in sequences of data. It is particularly effective for tasks involving time series or natural language processing, as it can remember information for long periods and forget irrelevant data. This ability helps it manage the vanishing gradient problem often encountered in traditional RNNs (Recurrent Neural Networks). LSTMs achieve this through a unique architecture that includes memory cells and gates. These components regulate the flow of information, allowing the network to decide what to keep or discard. This makes LSTMs suitable for applications like speech recognition, language translation, and stock price prediction.