NLP / word-embeddings

Word embeddings

Distributional hypothesis, Word2Vec, CBOW, Skip-gram, negative sampling, GloVe, FastText, subwords, and OOV.

shellbackend needed later
Focus
  • CBOW vs Skip-gram
  • negative sampling
  • subword embeddings
  • analogies
Future feature idea

Word2Vec/FastText embedding space with analogy and out-of-vocabulary examples.

Self-check
  1. What is the core job of "Word embeddings"?
  2. Which common mistake would break a production implementation of this topic?
  3. Which inputs or limits must be validated before the interactive feature ships?
  4. What is the smallest test that proves the future implementation behaves correctly?
  5. When does this module really need backend compute, and when is a UI simulation enough?
Implementation notes
  • Start with one focused feature, not a full course inside one page.
  • All public inputs must be typed, bounded, and covered by reject-case tests.
  • If a model, dataset, or job is added, document source, license, limits, and fallback.
  • The interaction must explain the topic rather than serve as decoration.