Word2Vec: Skip-gram and CBOW
Word2Vec theory, gensim implementation, vector arithmetic (king - man + woman = queen).
From Words to Vectors
Machine learning needs numbers, but words are symbols. Word embeddings map each word to a dense vector so that similar words sit close together in vector space.
The Distributional Hypothesis
Word2Vec rests on the idea that words appearing in similar contexts have similar meanings. By learning to predict context, the model captures meaning in the vectors.
All lessons in this course
- Word2Vec: Skip-gram and CBOW
- GloVe and FastText Embeddings
- Text Classification with BERT
- Semantic Similarity and Sentence Embeddings