Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) that is specifically designed to handle sequential data, such as time series, speech, and text. LSTMs are capable of learning order dependence in sequence prediction problems. They use a memory cell and gates to control the flow of information, allowing them to selectively retain or discard information as needed and thus avoid the vanishing gradient problem that plagues traditional RNNs.
Some key features of LSTMs include:
- Memory cell: This is the core component of an LSTM network. It stores information over time and interacts with the gates to regulate the flow of information.
- Gates: LSTMs have three types of gates - input, output, and forget gates. These gates control the flow of information into and out of the memory cell.
- Vanishing gradient problem: LSTMs are designed to overcome the vanishing gradient problem that occurs in traditional RNNs when gradients become too small to be useful for learning.
LSTMs have been used in a wide range of applications, including natural language processing tasks such as language modeling, machine translation, and text summarization. They are also used in speech recognition, handwriting recognition, video gaming, and healthcare.
