Word2Vec: A Deeper Understanding of Word Embeddings and Their Training Methods
Word2Vec is a model for learning word embeddings, which converts words and their semantics into vectors through neural networks. Word2Vec provides two training methods: CBOW and Skip-gram, and improves efficiency through Negative Sampling and Subsampling technologies. This article will introduce the basic principles and training methods of Word2Vec.