🌱Introduction
⛓ Basics of Sequence Models
Last updated
⛓ Basics of Sequence Models
Last updated
Sequences are data structures where each example could be seen as a series of data points, for example 🧐:
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)
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