Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN) architecture that is capable of learning long-term dependencies. They are particularly useful for tasks involving sequential data, such as time series prediction, natural language processing, and speech recognition.

Key Components of LSTM

  • Input Gate: Determines which information from the previous time step should be kept.
  • Forget Gate: Decides what information should be discarded from the previous time step.
  • Cell State: Carries information between time steps.
  • Output Gate: Controls what information is output to the next layer.

How LSTM Works

  1. Input Gate: The input gate decides which part of the previous hidden state should be retained. It is controlled by the sigmoid activation function, which outputs a value between 0 and 1.
  2. Forget Gate: The forget gate determines what information should be discarded from the previous time step. It also uses the sigmoid activation function.
  3. Cell State: The cell state is the core of the LSTM network. It carries information between time steps and is updated based on the input gate, forget gate, and the new input.
  4. Output Gate: The output gate determines what information should be output to the next layer. It is controlled by the sigmoid activation function and the tanh activation function.

Applications of LSTM

  • Time Series Prediction: Forecasting stock prices, weather patterns, and other time-dependent data.
  • Natural Language Processing: Language translation, sentiment analysis, and text generation.
  • Speech Recognition: Transcribing spoken words into written text.

LSTM Diagram

For more information on LSTM networks, you can visit our Deep Learning Tutorial.