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Sample concept cards from Attention Is All You Need

Query Q Key K Value V Head 1 Attention Score Softmax Head 2 Attention Score Softmax ⋯ Head h Attention Score Softmax Concat + Linear Output Attended
Attention Is All You Need

Multi-Head Attention

Parallel attention over different representation subspaces

min θ₁ θ₂ θ₃ Loss (J) θ (params) Update rule: θ ← θ − α · ∇J(θ) Current position Gradient step
Deep Learning (Goodfellow et al.)

Gradient Descent

Iterative optimisation by following the steepest downhill slope on the loss surface

ENCODER (×6) Multi-Head Self-Attention Add & Norm Feed-Forward Network K, V DECODER (×6) Masked Multi-Head Attention Cross-Attention (enc K,V) Feed-Forward Network Linear + Softmax → Output
Attention Is All You Need

Transformer Architecture

Encoder-decoder structure using only self-attention and feed-forward layers

Every concept, fully unpacked

XAI Visual Diagrams

AI-generated SVG diagrams showing how each concept actually works — not what the paper says, but the mechanism.

Narrated Explainer Clips

45-90 second animated clips with AI narration. The visual comes alive as the concept is explained.

Adaptive Quizzes

3 questions per concept. Short answers evaluated by Gemini. Your confidence score updates in real time.

Shareable Study Packs

One link bundles all concepts from a paper into a shareable deck. Built for virality.

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