LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) capable of learning order dependence in sequence prediction problems. LSTMs are designed to overcome the limitations of traditional RNNs by incorporating memory cells that can maintain information over long periods. Each LSTM cell contains gates that regulate the flow of information, allowing the network to learn which data to remember and which to forget. This architecture makes LSTMs particularly effective for tasks involving sequential data, such as time series forecasting, natural language processing, and speech recognition. LSTMs can capture long-term dependencies and patterns in data, making them suitable for complex sequence prediction problems where traditional RNNs struggle.