PART X — Beyond the Basics
mlflow
January 08, 2026
Chapter 21: Limitations & Future of LLMs Goal: Honest perspective Topics Covered: Hallucinations Bias Energy costs Futu...
Thursday, January 8, 2026
PART IX — Deployment & Real-World Use
mlflow
January 08, 2026
Chapter 20: Inference & Deployment Goal: From training to production Topics Covered: Inference vs training Quantization Mo...
PART VIII — Fine-Tuning & Alignment
mlflow
January 08, 2026
Chapter 18: Fine-Tuning LLMs Goal: Adapt base models Topics Covered: Supervised fine-tuning Instruction tuning Domain adaptati...
PART VII — Scaling & Performance
mlflow
January 08, 2026
Chapter 16: Scaling Laws in LLMs Goal: Explain why bigger works Topics Covered: Parameters vs data vs compute Empirical scaling l...
PART VI — Model Training
mlflow
January 08, 2026
Chapter 13: Training Objective & Loss Functions Goal: Define what the model learns Topics Covered: Next-token prediction Lang...
PART V — Transformer Architecture (The Core)
mlflow
January 08, 2026
Chapter 8: Why Transformers Replaced RNNs Goal: Explain the breakthrough Topics Covered: Limitations of RNNs and LSTMs Paralleliz...
PART IV — Embeddings: Turning Tokens into Meaning
mlflow
January 08, 2026
Chapter 7: Embeddings and Vector Representations Goal: Explain how meaning is stored Topics Covered: What embeddings are Dense ve...