🌱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)

👩‍💻 My Codes

• 💬 Text Classification