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Replying to @Bob Dylan 💬 Challenge accepted! Unpacking #transformers in 3 (oversimplified) steps: 1️⃣ Input embedding and positional encoding (turning words into context-aware numbers) 2️⃣ Self-attention (weighing word importance multiple times in parallel to understand the context) 3️⃣ Output prediction (next-token prediction based on the word with the highest #probability) Link to an interactive explainer in the comments. Thanks for the question!
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Replying to @Bob Dylan 💬 Challenge accepted! Unpacking #transformers in 3 (oversimplified) steps: 1️⃣ Input embedding and positional encoding (turning words into context-aware numbers) 2️⃣ Self-attention (weighing word importance multiple times in parallel to understand the context) 3️⃣ Output prediction (next-token prediction based on the word with the highest #probability) Link to an interactive explainer in the comments. Thanks for the question!
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AI • Machine Learning • Tech on Instagram: "Struggling to Understand Machine Learning? Join 7000+ Others in our Weekly AI Newsletter—educational, easy to understand, math included, and completely free (link in bio 🔗). Rotary Position Embeddings (RoPE) encode position information by applying a rotation matrix directly to the query and key vectors in attention. RoPE is an alternative to adding positional values to the embeddings, done in standard positional encoding. In RoPE, each token position
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AI • Machine Learning • Tech on Instagram: "Struggling to Understand Machine Learning? Join 7000+ Others in our Weekly AI Newsletter—educational, easy to understand, math included, and completely free (link in bio 🔗). Rotary Position Embeddings (RoPE) encode position information by applying a rotation matrix directly to the query and key vectors in attention. RoPE is an alternative to adding positional values to the embeddings, done in standard positional encoding. In RoPE, each token position
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