"LSTM (Long Short-Term Memory) networks are a type of Recurrent Neural Network (RNN) designed to learn long-term dependencies in sequential data such as text, speech, or time-series data. They address the vanishing gradient problem that traditional RNNs often face. An LSTM cell contains a memory state and three main gates: Forget Gate: Decides what information from the previous memory should be discarded. Input Gate: Determines what new information should be added to the memory. Output Gate: Controls what information from the memory is passed to the next time step or used as the output. By selectively keeping or forgetting information, LSTMs can retain relevant context over longer sequences. However, LSTMs have some limitations: Training can be slow because they process sequence elements step by step, making parallelization difficult. They can be computationally expensive and require significant memory for long sequences. Performance may degrade on very long dependencies, even though they improve on standard RNNs. They have many parameters, increasing the risk of overfitting and requiring more data to train effectively. More recent architectures like Transformers often outperform LSTMs on many natural language processing tasks because they can model long-range relationships more efficiently and support parallel computation. In practice, I would consider using LSTMs for sequential prediction tasks like sentiment analysis, language modeling, or time-series forecasting, but I would also evaluate Transformer-based models depending on the problem and available resources."