Neural-Networks

Introduction: The Scaling Dilemma Traditional transformer models face a fundamental trade-off: to increase model capacity, you must scale all parameters proportionally. If you want a smarter model, every single token must pass through every single parameter. This is dense activation, and it’s extremely expensive. Enter Mixture-of-Experts (MoE): a revolutionary architecture that achieves massive model capacity while keeping computational costs manageable through sparse activation. Models like GPT-4, Mixtral, and Switch Transformer use MoE to reach trillion-parameter scales while using only a fraction of those parameters per token. ...

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! ...