Applied Deep Learning – Class 43 | Self Attention Mathematical Formula

Описание: In this session of Applied Deep Learning, we explore the mathematical formula of self-attention as presented in the “Attention Is All You Need” paper.

This lecture is theory-only and focuses on deriving and understanding the core equations that make self-attention work in transformer models.

📚 In this lecture, we cover:

🔹 The Self-Attention Equation
We break down the fundamental formula from the paper:

Attention(Q, K, V) = softmax((Q · Kᵀ) / √dₖ) · V

…and explain what each term means, why the scaling factor √dₖ matters, and how softmax transforms similarity scores into attention weights.

🔹 Why This Formula Works
Learn how:
✔ Queries compare with keys to produce relevance scores
✔ Scaling prevents overly large gradients
✔ Softmax transforms scores into probabilities
✔ Weighted values produce contextualized outputs

🔹 Intuition Behind Each Step
Rather than just memorizing equations, we explain the meaning behind them — how words in a sentence attend to each other, how attention weights are computed, and how output vectors are formed.

🔹 Connection to Transformers
This formula is the centerpiece of:
✔ Self-Attention
✔ Scaled Dot-Product Attention
✔ The entire Transformer architecture

This session gives you the mathematical grounding necessary before moving to Multi-Head Attention and full Transformer implementation.

📂 Notebook Link:
https://github.com/GenEd-Tech/Applied...

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