🤖 Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture designed to address the vanishing gradient problem in traditional RNNs. Introduced by Hochreiter & Schmidhuber in 1997, LSTM networks excel at learning long-term dependencies through specialized memory cells and gating mechanisms (input, forget, output gates).
Key Features
- 📈 Memory Retention: Maintains information over long sequences via a cell state
- 🧩 Three Gates:
- 🔐 Forget Gate (controls what information to discard)
- 🧠 Input Gate (manages new information to store)
- 🚪 Output Gate (decides what to output)
- ⏳ Temporal Dynamics: Captures sequential patterns in time-series data, text, etc.
Applications
- 📖 Natural Language Processing (e.g., machine translation, text generation)
- 🎵 Speech Recognition
- 📊 Financial Forecasting
- 📈 Anomaly Detection in Time Series
Related Resources
For deeper exploration: