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Lecture Notes
CS 261 · Lecture 18: Transformer Architecture & Attention Mechanisms
Dr. Amanda Foster — Stanford University, Computer Science Department
Key Concepts Covered
Self-Attention Mechanism
Each token in the input sequence attends to every other token simultaneously. The attention score is computed as Q·Kᵀ / √dₖ — where Q = Query, K = Key, V = Value. Higher dk leads to smaller gradients if not scaled.
Multi-Head Attention
Instead of one attention function, run h parallel attention operations with different learned projections. Each head learns to attend to different aspects of the input. Outputs are concatenated then linearly projected.
Positional Encoding
Transformers have no inherent sense of sequence order — positional encodings (sinusoidal or learned) are added to token embeddings to inject position information before the attention layers.
Chapters
00:00 — Introduction: limitations of RNNs that Transformers solve
11:42 — The Attention mechanism: intuition and formal definition
28:55 — Multi-head attention and why one head isn't enough
47:30 — Positional encodings: sinusoidal vs learned
1:06:14 — Feed-forward sublayers and layer normalisation
1:18:40 — Q&A and problem set 3 walkthrough
Also generated
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Lecture Notes
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Full Transcript
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Key Concepts
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TL;DR Summary
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Q&A Pairs
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Chat with Lecture
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