Sequence models are a class of machine learning algorithms designed to process and generate sequential data, such as time series, speech, text, or video. They are widely used in natural language processing (NLP), speech recognition, and other sequential prediction tasks.
🔑 Key Concepts
- Recurrence: Models use recurrence relations to maintain memory of previous inputs.
- Attention Mechanism: Enhances performance by focusing on relevant parts of the input sequence.
- Variants: Common types include Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs).
📌 Applications
- Speech Recognition: Converting audio signals into text.
- Machine Translation: Translating text between languages.
- Time Series Forecasting: Predicting future values based on historical data.