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So as you're probably already aware of, CBOW and Skip-gram are just mirrored versions of each other. CBOW is trained to predict a single word from a fixed window size of context words, whereas Skip-...

Word embeddings are the results of learning from deep learning algorithms, which can learn characters from data through feature extraction. One implementation of word embedding is word2vec. Word2vec ...

Whether linear maps are "required" is an extremely difficult question -- I'm sure there's a number of ways to implement the general idea for the skip gram architecture. But this is more of a ...

The following figure from this article can be helpful: This figure represents "Skip-Gram model structure. Current center word is 'passes'".