Attention is All You Need: Visualized and Explained
Introduction: The Paper That Changed Everything In 2017, Google researchers published “Attention is All You Need”, introducing the Transformer architecture. This single paper: Eliminated recurrence in sequence modeling Introduced pure attention mechanisms Enabled massive parallelization Became the foundation for GPT, BERT, and all modern LLMs Let’s visualize and demystify this revolutionary architecture, piece by piece. The Problem: Sequential Processing is Slow Before Transformers: RNNs and LSTMs graph LR A[Word 1The] --> B[Hidden h1] B --> C[Word 2cat] C --> D[Hidden h2] D --> E[Word 3sat] E --> F[Hidden h3] style B fill:#e74c3c style D fill:#e74c3c style F fill:#e74c3c Problem: Sequential processing—each step depends on the previous. Can’t parallelize! ...