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⛓ Basics of Sequence Models |
- Sequences are data structures where each example could be seen as a series of data points, for example 🧐:
| Task | Input X | Output Y | Type |
|---|---|---|---|
| 💬 Speech Recognition | Wave sequence | Text sequence | Sequence-to-Sequence |
| 🎶 Music Generation | Nothing / Integer | Wave Sequence | One-to_Sequence |
| 💌 Sentiment Classification | Text Sequence | Integer Rating (1➡5) | Sequence-to-One |
| 🔠 Machine Translation | Text Sequence | Text Sequence | Sequence-to-Sequence |
| 📹 Video Activity Recognition | Video Frames | Label | Sequence-to-One |
- Since we have labeled data X and Y so all of these tasks are addressed as Supervised Learning 👩🏫
- Even in Sequence-to-Sequence tasks lengths of input and output can be different ❗
- Machine learning algorithms typically require the text input to be represented as a fixed-length vector 🙄
- Thus, to model sequences, we need a specific learning framework able to:
- ✔ Deal with variable-length sequences
- ✔ Maintain sequence order
- ✔ Keep track of long-term dependencies rather than cutting input data too short
- ✔ Share parameters across the sequence (so not re-learn things across the sequence)