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:
- Cell: The core unit of an LSTM, responsible for maintaining memory over arbitrary time intervals.
- Input Gate: Determines which new information to store in the current state.
- Output Gate: Controls which information from the current state to output.
- 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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