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Introduction

⛓ ‍Basics of Sequence Models

⛓ Sequence Models In General

  • 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 ❗

🤔 Why Do We Need Sequence Models?

  • 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)

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