Long short-term memory

LSTM Cell

Peephole Long Short-Term Memory

Long short-term memory (LSTM) is a type of recurrent neural network (RNN) architecture used in the field of deep learning. LSTM networks are well-suited to classifying, processing, and making predictions based on time series data, as they are capable of learning long-term dependencies. This is achieved through a special structure that allows them to maintain information in memory for long periods.

Architecture

LSTM networks are composed of units called LSTM cells. Each LSTM cell contains three main components: an input gate, a forget gate, and an output gate. These gates regulate the flow of information into and out of the cell, allowing the network to retain or discard information as needed.

Input Gate

The input gate controls the extent to which new information flows into the cell state. It decides which values from the input will be updated in the cell state.

Forget Gate

The forget gate determines which information from the cell state should be discarded. This gate is crucial for preventing the cell state from becoming overloaded with irrelevant information.

Output Gate

The output gate controls the output of the cell state. It decides which parts of the cell state will be output to the next layer or the next time step.

Training

LSTM networks are typically trained using backpropagation through time (BPTT), a variant of the backpropagation algorithm. This involves unrolling the LSTM network through time and computing gradients for each time step.

Applications

LSTM networks have been successfully applied in various fields, including:

• Natural language processing (NLP)
• Speech recognition
• Time series forecasting
• Anomaly detection
• Music composition

Advantages

LSTM networks offer several advantages over traditional RNNs:

• Ability to learn long-term dependencies
• Reduced risk of the vanishing gradient problem
• Improved performance on tasks involving sequential data

See Also

• Recurrent neural network
• Gated recurrent unit
• Deep learning
• Artificial neural network
• Backpropagation through time