An in-depth Stanford CS229 lecture on building large language models (LLMs), covering architecture, training, and deployment.
Overview
This Stanford CS229 Machine Learning lecture, led by Chris Chute and hosted by Andrew Ng, dives into the foundational principles and practical aspects of building Large Language Models (LLMs). It explores transformer architecture, pre-training, fine-tuning, data curation, inference optimization, and ethical considerations. Ideal for students, researchers, and developers seeking deep insights into LLM development, this session makes complex concepts accessible while maintaining technical depth from leading experts.