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Topic · language understanding

language understanding

The 28 most recent episodes and tracks on this topic.

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  1. Stanford XCS224U: NLU I Information Retrieval, Part 1: Guiding Ideas I Spring 2023Stanford XCS224U: Natural Language Understanding I Spring 2023Notes
  2. Stanford XCS224U: Natural Language Understanding I Homework 2 I Spring 2023Stanford XCS224U: Natural Language Understanding I Spring 2023Notes
  3. Stanford XCS224U: NLU I Contextual Word Representations, Part 10: Wrap-up I Spring 2023Stanford XCS224U: Natural Language Understanding I Spring 2023Notes
  4. Stanford XCS224U: NLU I Contextual Word Representations, Part 9: Distillation I Spring 2023Stanford XCS224U: Natural Language Understanding I Spring 2023Notes
  5. Stanford XCS224U: NLU I Contextual Word Representations, Part 8: Seq2seq Architectures I Spring 2023Stanford XCS224U: Natural Language Understanding I Spring 2023Notes
  6. Stanford XCS224U: NLU I Contextual Word Representations, Part 7: ELECTRA I Spring 2023Stanford XCS224U: Natural Language Understanding I Spring 2023Notes
  7. Stanford XCS224U: NLU I Contextual Word Representations, Part 6: RoBERTa I Spring 2023Stanford XCS224U: Natural Language Understanding I Spring 2023Notes
  8. Stanford XCS224U: NLU I Contextual Word Representations, Part 5: BERT I Spring 2023Stanford XCS224U: Natural Language Understanding I Spring 2023Notes
  9. Stanford XCS224U: NLU I Contextual Word Representations, Part 4: GPT I Spring 2023Stanford XCS224U: Natural Language Understanding I Spring 2023Notes
  10. Stanford XCS224U: NLU I Contextual Word Representations, Part 3: Positional Encoding I Spring 2023Stanford XCS224U: Natural Language Understanding I Spring 2023Notes
  11. Lecture 15 – Presenting Your Work | Stanford CS224U: Natural Language Understanding | Spring 2019Stanford CS224U: Natural Language Understanding | Spring 2019Notes
  12. Lecture 14 – Contextual Vectors | Stanford CS224U: Natural Language Understanding | Spring 2019Stanford CS224U: Natural Language Understanding | Spring 2019Notes
  13. Lecture 13 – Evaluation Metrics | Stanford CS224U: Natural Language Understanding | Spring 2019Stanford CS224U: Natural Language Understanding | Spring 2019Notes
  14. Lecture 12 – Evaluation Methods | Stanford CS224U: Natural Language Understanding | Spring 2019Stanford CS224U: Natural Language Understanding | Spring 2019Notes
  15. Lecture 11 – Semantic Parsing | Stanford CS224U: Natural Language Understanding | Spring 2019Stanford CS224U: Natural Language Understanding | Spring 2019Notes
  16. Lecture 10 – Grounding | Stanford CS224U: Natural Language Understanding | Spring 2019Stanford CS224U: Natural Language Understanding | Spring 2019Notes
  17. Lecture 1 – NLU Course Overview | Stanford CS224U: Natural Language Understanding | Spring 2019Stanford CS224U: Natural Language Understanding | Spring 2019Notes
  18. Lecture 2 – Word Vectors 1 | Stanford CS224U: Natural Language Understanding | Spring 2019Stanford CS224U: Natural Language Understanding | Spring 2019Notes
  19. Lecture 4 – Word Vectors 3 | Stanford CS224U: Natural Language Understanding | Spring 2019Stanford CS224U: Natural Language Understanding | Spring 2019Notes
  20. Lecture 3 – Word Vectors 2 | Stanford CS224U: Natural Language Understanding | Spring 2019Stanford CS224U: Natural Language Understanding | Spring 2019Notes
  21. 8 deep neural networks are our friendsLxMLS 2016Notes
  22. 9 memory networks for language understandingLxMLS 2016Notes
  23. 6 syntax and parsing ILxMLS 2016Notes
  24. 5 learning structured predictorsLxMLS 2016Notes
  25. 7 turbo parser redux from dependencies to constituentsLxMLS 2016Notes
  26. 3 sequence modelsLxMLS 2016Notes
  27. 2 introduction to machine kearning linear learnersLxMLS 2016Notes
  28. 4 machine translation as sequence modellingLxMLS 2016Notes

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