Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) designed to overcome the vanishing gradient problem in standard RNNs.
LSTMs are excellent at capturing long-term dependencies in sequential data, making them ideal for text, speech, and time series analysis.
How LSTM Works
- Cell State: Maintains long-term memory throughout the sequence.
- Gates: Control information flow:
- Forget Gate: Decides what to discard from memory
- Input Gate: Decides what new information to add
- Output Gate: Decides what part of memory to output
- Sequence Processing: Combines hidden states and cell states to produce outputs while retaining important information.
Advantages of LSTM
- Captures long-term dependencies in sequences
- Solves vanishing gradient issues in RNNs
- Works well with variable-length sequences
- Flexible for NLP, speech, and time series tasks
Disadvantages
- Computationally intensive
- Requires more data and training time than standard RNNs
- Complex architecture can be harder to implement
Real-World Examples
- Language translation (Google Translate)
- Speech recognition (voice assistants)
- Text generation (chatbots, story generation)
- Stock price prediction
- Video and sequence analysis
Conclusion
LSTM networks enhance RNNs by remembering long-term information, making them indispensable for complex sequential tasks in AI.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/