This article explores the power of LSTM in deep learning, a special kind of recurrent neural network algorithm that is invaluable in a range of applications. It delves into the basic principles and internal workings of LSTM, highlighting its unique structure that enables it to remember information for extended periods. Additionally, it discusses how LSTMs differ from traditional RNNs and their ability to overcome the vanishing gradient problem.
