dobriban/Principles-of-AI-LLMs
Materials for the course Principles of AI: LLMs at UPenn (Stat 9911, Spring 2025). LLM architectures, training paradigms (pre- and post-training, alignment), test-time computation, reasoning, safety and robustness (jailbreaking, oversight, uncertainty), representations, interpretability (circuits), etc.
This is a comprehensive collection of materials from the 'Principles of AI: LLMs' course at UPenn, designed for students and researchers. It provides lecture notes, presentations, and readings that cover the foundational concepts of Large Language Models, from their architectures to advanced topics like training, reasoning, and safety. The target audience includes graduate students, academics, and professionals looking to deepen their understanding of LLMs beyond basic usage.
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Use this if you are a student, researcher, or AI practitioner seeking in-depth academic resources to understand the underlying principles and advanced concepts of Large Language Models.
Not ideal if you are looking for a hands-on coding tutorial or a high-level, non-technical introduction to using LLMs in practical applications.
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