Why is LSTM better than RNN

Long Short-Term Memory (LSTM) networks outshine traditional RNNs by effectively managing long-range dependencies. Their unique architecture, featuring memory cells and gates, allows them to retain crucial information while mitigating the vanishing gradient problem.

What is the difference between LSTM and RNN

LSTM and RNN are both neural networks designed for sequential data, but they differ in complexity. While RNNs struggle with long-term dependencies, LSTMs use memory cells to retain information over time, making them more effective for tasks like language modeling.

What is LSTM best used for

LSTM, or Long Short-Term Memory networks, excel in tasks involving sequential data. They shine in applications like language modeling, speech recognition, and time series forecasting, where understanding context and long-range dependencies is crucial for accurate predictions.

What is LSTM in deep learning

Long Short-Term Memory (LSTM) networks are a type of recurrent neural network designed to remember information for extended periods. They excel in tasks involving sequential data, such as language processing and time series prediction, by effectively managing long-range dependencies.