• After talking about human language and word meaning, we’ll introduce the ideas of the word2vec algorithm for learning word meaning.
  • Going from there, we’ll kind of concretely work through how you can work out objective function gradients with respect to the word2vec algorithm.
  • We shall look into how optimization works.
  • Finally, we develop a sense of how these word vectors work and what you can do with them.
  • Key takeaway
    • The really surprising result that word meaning can be represented not perfectly, but rather well by a large vector of real numbers gives a sense of how amazing deep learning word vectors are.

What do you hope to learn in this course?


If you found our work useful, please cite it as:

  title   = {},
  author  = {Chadha, Aman},
  journal = {Distilled Notes for Stanford CS224n: Natural Language Processing with Deep Learning},
  year    = {2021},
  note    = {\url{https://aman.ai}}