Visualization

Introduction: Words as Numbers How do language models understand meaning? The answer lies in embeddings: representing words, sentences, and entire documents as vectors of numbers in high-dimensional space. In this space: Similar words cluster together Analogies emerge as geometric relationships Meaning becomes computable through vector arithmetic Let’s visualize this invisible geometry where meaning is distance. From Words to Vectors Traditional Approach: One-Hot Encoding graph TB A[Vocabulary:cat, dog, king, queen, apple] A --> B[cat = 1,0,0,0,0] A --> C[dog = 0,1,0,0,0] A --> D[king = 0,0,1,0,0] A --> E[queen = 0,0,0,1,0] A --> F[apple = 0,0,0,0,1] style B fill:#e74c3c style C fill:#e74c3c style D fill:#e74c3c style E fill:#e74c3c style F fill:#e74c3c Problem: No semantic relationship! ...

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! ...