Applied Deep Learning – Class 43 | Self Attention Mathematical Formula скачать в хорошем качестве

Applied Deep Learning – Class 43 | Self Attention Mathematical Formula

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... 👍 Like, Share & Subscribe for more AI, Deep Learning & NLP content 💬 Comment if you want the next session on Multi-Head Attention #DeepLearning #SelfAttention #MathOfAttention #Transformer #NLP #MachineLearning #AI #AppliedDeepLearning