Context-Aware Representation
How Self-Attention Builds Contextual Representations
Observe step 5.2 and 5.3 below: isolated token vectors look across the sentence to assign attention weights (e.g. 'capital' attending to 'India'), producing context-enriched output vectors.
What is Context-Aware Representation? Why does it exist? Where does it appear in Self-Attention?
Draw or describe a visual model of Context-Aware Representation. What does it look like conceptually?
Work through a concrete manual example of Context-Aware Representation with real numbers or pseudocode.
Implement Context-Aware Representation from scratch in Python. Keep it minimal — just enough to see it work.
Try Context-Aware Representation on a real example. Vary the inputs and observe what changes.
How is Context-Aware Representation different from related concepts in Self-Attention? What are the trade-offs?
Explain Context-Aware Representation in plain language using an analogy. Could a non-expert follow it?
Build a small project or demo that uses Context-Aware Representation as a core component.
🔨 M-05 MultiHeadAttention 🔁 self-attention → state history design → return to
agent/state_tracker.py
📋 full plan →