So I am learning Word2Vec for the first time and my question is quite basic: How to know what approach to use? I.e, [Word2Vec in Tensorflow](http://adventuresinmachinelearning.com/word2vec-tutorial-tensorflow/) or [Word2Vec trained with Gensim](http://adventuresinmachinelearning.com/gensim-word2vec-tutorial/) ? 

In what cases would implementing it through the more manual first approach be useful vs. the second one? If there is already an easier way to train a word2vec model using gensim, why is that not used always? 

Furthermore, what is the benefit in using a pre-trained model like the [Google News dataset](http://mccormickml.com/2016/04/12/googles-pretrained-word2vec-model-in-python/)? What happens when there are words that are not included in the news dataset? 

Sorry if this question is basic, I just want to get a clearer grasp of the overall picture.