📦 Module 2: Representing Meaning — Embeddings & Positional Awareness

[malibayram]

📦 Module 2: Representing Meaning — Embeddings & Positional Awareness

This module covers token embeddings and positional encodings, fundamental components for representing text in neural networks.

Tasks to Complete:

• Lesson 2.1 — From Tokens to Vectors: The Magic of Embeddings

  • Explain one-hot vs learned embeddings
  • Introduce distributional semantics (Word2Vec intro)
  • Demonstrate why embeddings capture semantic meaning

• Lesson 2.2 — Implementing Token Embeddings

  • Use nn.Embedding in PyTorch
  • Complete shape walkthrough and tensor operations
  • Create embedding layer implementation

• Lesson 2.3 — The Problem of Order: Why Position Matters

  • Explain why position information is crucial
  • Demonstrate sequence order problems
  • Introduce positional encoding concepts

• Lesson 2.4 — Implementing & Visualizing Positional Encodings

  • Implement sinusoidal positional encoding
  • Add positional encodings to token embeddings
  • Create visualizations of positional patterns
  • Test different positional encoding strategies

Deliverables:

• 4 video lectures (~25 minutes each)
• Token embeddings implementation notebook
• Positional encoding notebook with visualizations
• Combined embedding + positional encoding module
• Module quiz

Key Implementation Files:

• embeddings.py - Token embedding implementation
• positional_encoding.py - Positional encoding implementation
• Visualization notebook for embeddings and positions

Resources:

• Word2Vec paper references
• Sinusoidal positional encoding mathematics
• PyTorch embedding documentation