EP 32 | CME295: Large Language Models (LLMs)
from AI Bites: The Academic Series Hosted by Jack Lakkapragada
What happens when you take the Transformer architecture and scale it up with massive amounts of compute and data? You get Large Language Models (LLMs). We wrap up Week 1 by connecting the base architecture to the modern AI tools you use every day. Key Topics: Scaling Up: How adding more parameters and training data fundamentally changes the capability of a Transformer. Pre-training vs. Fine-tuning: The two-step process of teaching a model the fundamental rules of language, and then narrowing its focus to be a helpful assistant. Emergent Abilities: How scaling leads to unexpected capabilities, moving from simple text prediction to complex reasoning and coding. Note: This is an AI-generated study resource created via NotebookLM based on the CME295 curriculum and personal study notes.
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