Understanding Large Language Model Architectures and Scaling

As artificial intelligence continues to evolve, understanding what happens under the hood of large language models is essential for any aspiring AI engineer. This course demystifies the core structural designs and mathematical principles that govern how modern models are built, scaled, and optimized. Through clear, written explanations and structured code walkthroughs, you will transition from a high-level user to a practitioner who understands model configurations. You will gain a deep understanding of attention mechanisms, parameter scaling, and efficiency techniques used in industry-standard models. What you will learn: 1. Learn the foundational mathematics and structural components of transformer-based architectures. 2. Understand scaling laws and how compute, data size, and parameter counts influence model performance. 3. Explore Mixture-of-Experts (MoE) designs and how they enable efficient sparse routing. 4. Analyze long-context strategies, including rotary position embeddings and attention optimization. 5. Practice evaluating model configurations and resource requirements for training and inference. 6. Discover modern optimization trends like quantization and memory-efficient attention mechanisms. The course begins with essential terminology and the basic building blocks of neural networks before guiding you through scaling equations, MoE routing, and advanced context-handling techniques. You will read detailed conceptual breakdowns and analyze structural code representations to solidify your engineering knowledge. This course is designed for software engineers, data analysts, and tech enthusiasts who want to build a theoretical and practical foundation in AI model design. No prior deep learning experience is required, though basic familiarity with programming concepts is helpful. Start reading today to master the architectural principles driving the future of artificial intelligence.