🧠 Day 7/30 Building a Large Language Model from Scratch and today I learnt about Building Self-Attention from Scratch! Today I learnt how to create a simple self-attention mechanism — no trainable weights, just pure math magic ✨ Here’s the breakdown: 🔹 Goal: Compute a context vector for each token that blends in info from all other tokens in the input. 🔹 Step 1: Use the dot product between the query (e.g., token 2) and all tokens to get attention scores. 🔹 Step 2: Normalize these scores using softmax to get attention weights (how much each token contributes). 🔹 Step 3: Multiply each input vector by its attention weight and sum them up to get the context vector. 🧪 Example input: “Your journey starts with one step.” Each word is a 3D embedding. Context vector for “journey” ≈ [0.4419, 0.6515, 0.5683] It’s amazing how attention lets models focus on relevant parts of a sentence — like how we do when understanding language! 📚 Next up: generalizing attention across all tokens and ...