Learn NLP the Stanford Way -- Lesson 2

In the previous post, we introduced NLP. To find out word meanings with the Python programming language, we used the NLTK package and worked our way into word embeddings using the gensim package and Word2vec. Since we only touched the Word2Vec technique from a 10,000-feet overview, we are now going to dive deeper into the training method to create a Word2vec model. The Word2vec (Mikolov et al. 2013)[1][2] is not a singular technique or algorithm. It's actually a family of neural network architectures and optimization techniques that can produce good results learning embeddings for large datasets.