How are the embedding and context matrices created and updated in word embedding?
I am struggling to understand how word embedding works, especially how the embedding matrix $W$ and context matrix $W'$ are created/updated. I understand that in the Input we may have a one-hot encoding of a given word, and that in the output we may have the word the most likely to be nearby this word $x_i$
Would you have any very simple mathematical example?
Topic embeddings word-embeddings nlp
Category Data Science