This was the first chapter devoted to NLP. Appropriately, we started with the basic building blocks of most NLP algorithms today—the words and their context-based vector representations. We started with n-grams and the need to represent words as vectors. Then, we discussed the word2vec, fastText, and GloVe models. Finally, we implemented a simple pipeline to train an embedding model and we visualized word vectors with t-SNE.
In the next chapter, we'll discuss RNNs—a neural network architecture that naturally lends itself to NLP tasks.
