Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN) that is designed to overcome the vanishing gradient problem which is a common issue in traditional RNN. LSTM networks were first proposed in 1997 by Sepp Hochreiter and Jürgen Schmidhuber.
The main idea behind LSTM networks is the use of a memory cell that allows the network to selectively remember or forget information over long periods of time. The memory cell is composed of input, output, and forget gates which control how information flows into and out of the cell. The input gate determines which values should be updated in the current time step, the forget gate determines which values in the memory cell should be forgotten, and the output gate determines which values in the cell should be outputted for use.
The architecture of an LSTM network generally consists of multiple LSTM layers, with each layer containing multiple LSTM units. Each LSTM unit has three gates (input, forget, and output), a memory cell, and a hidden state. The input gate is responsible for determining which values should be updated in the current time step, the forget gate is responsible for deciding which values in the memory cell should be forgotten, and the output gate is responsible for deciding which values in the cell should be outputted.
The functionality of LSTM networks can be broken down into several steps. Firstly, the input is multiplied by the weights of the input gate, followed by an element-wise multiplication by the sigmoid function to determine the values that should be updated. The forget gate determines which values in the memory cell should be forgotten, while the output gate decides which values in the cell should be outputted. Finally, a new hidden state is calculated based on the input, output, and forget gates.
LSTM networks are particularly effective for handling sequential data since they can selectively remember or forget information over long periods of time. Applications of LSTM network include speech recognition, natural language processing, and handwriting recognition. For example, in speech recognition, an LSTM network can be used to classify a sequence of audio frames into phonemes, while in natural language processing, an LSTM network can be used to generate text or classify sentiment in text.