PART VII — Scaling & Performance

 

Chapter 16: Scaling Laws in LLMs

Goal: Explain why bigger works

Topics Covered:

• Parameters vs data vs compute

• Empirical scaling laws

• Emergent abilities

• Cost trade-offs

📌 Medium Post 16: Why Bigger Language Models Work Better

---

Chapter 17: Evaluation & Validation

Goal: Measuring intelligence

Topics Covered:

• Perplexity

• Benchmark datasets

• Overfitting detection

• Generalization

📌 Medium Post 17: How We Evaluate Language Models