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Introduction to Long Short-Term Memory (LSTM) networks

LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) architecture widely used in Deep Learning. Its primary strength lies in capturing long-term dependencies, making it ideal for sequence prediction tasks.

Key Components of an LSTM:

  1. Cell: The core unit of an LSTM, responsible for maintaining memory over arbitrary time intervals.
  2. Input Gate: Determines which new information to store in the current state.
  3. Output Gate: Controls which information from the current state to output.
  4. Forget Gate: Decides what information to discard from the previous state.

How LSTMs Work:

  • LSTMs process data sequentially and maintain a hidden state through time.
  • Forget gates selectively discard irrelevant information from the previous state.
  • Input gates decide which new information to incorporate into the current state.
  • Output gates allow the network to output relevant information, maintaining useful long-term dependencies for predictions.

Applications of LSTMs: LSTMs find applications in:

  • Handwriting recognition
  • Speech recognition
  • Machine translation
  • Speech activity detection
  • Robot control
  • Video games
  • Healthcare, and more.

In summary, LSTMs provide a powerful solution for handling sequential data, overcoming the vanishing gradient problem faced by traditional RNNs. Their ability to capture long-term dependencies makes them indispensable in various domains of artificial intelligence and machine learning.


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