Another quick lecture -- I've been asked many times for prereq's to my book and what you should know, so built a little lecture (with GLM 5.2) to cover some more basics.

Topics include:

00:00 Introduction & Course Prerequisites
01:37 Language Models Overview
02:47 The LM Head
04:29 Softmax & Log-Probabilities
06:13 Anatomy of an LM Training Example
06:37 Computing LLM Probabilities (+Phoebe the Dog)
09:52 Three Common Masks in Post-Training
11:03 A Small Decoding Review
12:14 Training an LM: Cross-Entropy
13:23 Optimization & Fine-Tuning
13:55 Pretraining to Midtraining to SFT Pipeline
15:25 Probability Essentials: KL Divergence & Entropy
19:36 Sigmoid & Pairwise Likelihood
20:29 Reinforcement Learning Framing (MDP)
22:28 Transitioning Tools into Post-Training
23:12 Recommended Resources & Wrap-Up
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Happy learning and I'm still taking questions from during the course for Q&A videos.