1. Source by Google
Project with Code: Word2Vec
 Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient Estimation of Word Representations in Vector Space. In Proceedings of Workshop at ICLR, 2013.
Note: The new model architectures:
 Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. Distributed Representations of Words and Phrases and their Compositionality. In Proceedings of NIPS, 2013.
Note: The Skip-gram Model with Hierarchical Softmax and Negative Sampling
 Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig. Linguistic Regularities in Continuous Space Word Representations. In Proceedings of NAACL HLT, 2013.
Note: It seems no more information
 Tomas Mikolov, Quoc V. Le, Ilya Sutskever. Exploiting Similarities among Languages for Machine Translation
Note: Intersting word2vec application on SMT
 NIPS DeepLearning Workshop NN for Text by Tomas Mikolov and etc.
2. Best explained with original models, optimizing methods, Back-propagation background and Word Embedding Visual Inspector
Youtube Video: Word Embedding Explained and Visualized – word2vec and wevi
3. Word2Vec Tutorials:
Word2Vec Tutorial by Chris McCormick:
a) Word2Vec Tutorial – The Skip-Gram Model
Note: Skip over the usual introductory and abstract insights about Word2Vec, and get into more of the details
Alex Minnaar’s Tutorials
The original article url is down, the following pdf version provides by Chris McCormick:
4. Learning by Coding
Distributed Representations of Sentences and Documents
Python Word2Vec by Gensim related articles:
Note: Simple but very powerful tutorial for word2vec model training in gensim.
5. Ohter Word2Vec Resources: