When we train computers to understand sequences of information (like sentences in a speech or notes in a music piece), it's crucial they remember what came before. Traditional methods, like recurrent neural networks, struggle with this, especially when they need to remember information over long periods or steps.The problem is like trying to follow a story where you keep forgetting the earlier parts. The longer the story, the more you forget, making it difficult to understand the whole picture. This happens because the \'signal\' (or error message) that helps the network learn, gets weaker as it moves back through each layer (imagine trying to hear a whisper from across a long tunnel).Long Short-Term Memory (LSTM) is a clever solution to this problem. It introduces a special way to process information that's akin to having a selective memory. It can choose what to remember and what to forget, thanks to components called \'gates.\' These gates control the flow of information, much like a valve controls the flow of water in a pipe.LSTM networks have a kind of internal conveyor belt that carries important information throughout the learning process. This means they can maintain a strong learning signal over many steps, which is like being able to remember every part of the story, no matter how long it is.This method has proven to be much more effective and efficient than previous ones, especially in tasks that require understanding or remembering information over long periods. It has been a significant breakthrough in teaching computers to process sequences, whether it be language, handwriting, or even music.