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Scaling laws + exam review
1:13:42
Transformer configurations & tokenization
1:12:01
Pretraining vs post-training
1:10:56
Mixture of experts language models
1:12:25
Transformer language models
1:10:56
Attention mechanisms
1:12:56
Training neural language models
1:10:36
Composition and decoding in neural language models
1:11:01
Neural language models
1:12:40
Basics of language models
1:09:56
Course introduction + history of NLP
1:08:26
UMD F25 NLP #23: Exam review
1:14:10
UMD F25 NLP #22: Memorization & factuality
1:10:05
UMD F25 NLP #21: LLM security
1:10:36
UMD F25 NLP #20: LLM agents
1:12:36
UMD F25 NLP #19: RAG + tool use
1:08:56
UMD F25 NLP #18: Benchmarking LLMs
1:12:06
UMD F25 NLP #17: Test-time scaling
1:10:25
UMD F25 NLP #16: GRPO and reasoning models
1:11:31
UMD F25 NLP #16: PPO
1:10:01
UMD F25 NLP #15: RL for LLMs
1:11:35
UMD F25 NLP #14: Reward models
1:13:01
UMD F25 NLP #13: Exam recap / limitations of SFT
1:10:05
UMD F25 NLP #12: Instruction tuning and PEFT
1:15:16
UMD F25 NLP #11: Scaling laws for LLMs
1:12:36
UMD F25 NLP #10: Position encodings for Transformer LMs
1:15:55
UMD F25 NLP #9: Tokenization for LLMs
1:13:35
UMD F25 NLP #8: How to use Transformers
1:10:56
UMD F25 NLP #7: Transformer language models
1:14:50
UMD F25 NLP #6: Attention mechanisms
1:10:35