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NLP with Deep Learning

Stanford CS224N · AI · Fall 2027 · Planned

Lectures

  1. Lecture 1: Introduction and Word Vectors (1:20:17)
  2. Lecture 2: Word Vectors and Language Models (1:19:12)
  3. Lecture 3: Backpropagation and Neural Networks (1:13:27)
  4. Lecture 4: Dependency Parsing (1:18:56)
  5. Lecture 5: Language Models and Recurrent Neural Networks (1:18:52)
  6. Lecture 6: LSTMs and Neural Machine Translation (1:17:09)
  7. Lecture 7: Attention and Choosing a Final Project (1:17:44)
  8. Lecture 8: Self-Attention and the Transformer (1:17:03)
  9. Lecture 9: Pretraining (1:18:46)
  10. Lecture 11: Natural Language Generation (1:18:24)
  11. Lecture 10: Post-training (1:19:42)
  12. Lecture 11: Benchmarking and Evaluation (1:24:24)
  13. Lecture 12: Efficient Training of Large Models (1:02:32)
  14. Lecture 13: Speech Brain-Computer Interfaces (1:12:49)
  15. Lecture 14: Reasoning and Language Model Agents (1:03:42)
  16. Lecture 15: After DPO, with Nathan Lambert (1:08:57)
  17. Lecture 16: ConvNets and Tree Recursive Neural Networks (1:11:55)
  18. Lecture 18: NLP, Linguistics, and Philosophy (1:16:26)
  19. Lecture 16: Multimodal Deep Learning, with Douwe Kiela (1:18:23)
  20. Lecture 19: Model Interpretability and Editing, with Been Kim (1:11:42)
  21. Python Tutorial (CS224N Review Session) (47:13)
  22. PyTorch Tutorial (CS224N Review Session) (47:00)
  23. Hugging Face Tutorial (CS224N Review Session) (47:57)

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