word2vec-demo

• This is a demo of word2vec skip-gram architecture
  • The sliding window size is 3
  • We use a center word (in one hot vector format) as an input to predict context words (in one hot vector format)
  • After the training is done, the hidden layer can be used as word embeddings and we can observe that similar words will be close to each other in vector space

Credits

• Forked from https://github.com/study-ml/word2vec-demo
• Credits for this fork: Daniel Ramos for IMAGINARY