Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) that is widely used in machine learning for sequential data modeling. LSTMs are designed to address the vanishing and exploding gradient problems that are often encountered in traditional RNNs.
LSTM networks are composed of memory cells that can maintain information for long periods of time. Each cell is controlled by three gates: an input gate, an output gate, and a forget gate. These gates regulate the flow of information into and out of the cell, as well as the forgetting of old information.
During training, LSTMs learn to update their cell state based on the input sequence and the previous hidden state. The input gate determines which parts of the input sequence should be added to the memory cell, while the forget gate decides which parts should be discarded. The output gate determines which parts of the cell state should be outputted to the next layer of the network.
The LSTM architecture has proven to be effective for various tasks, including speech recognition, natural language processing, and image captioning. One of the reasons for its success is its ability to capture long-term dependencies in the data, which is important for tasks that involve sequences with long gaps between relevant information.
In summary, the LSTM algorithm is a type of RNN that is designed to address the vanishing and exploding gradient problems. It uses memory cells that can maintain information for long periods of time and gates to control the flow of information into and out of the cell. LSTMs have proven to be effective for various sequential data modeling tasks.