In this chapter, we learned how to transform words in text into vector embeddings that retain the distributional semantics of the word. We also now have an intuition of why word embeddings exhibit this kind of behavior and why word embeddings are useful for working with deep learning models for text data.
We then looked at two popular word embedding schemes, word2vec and GloVe, and understood how these models work. We also looked at using gensim to train our own word2vec model from data.
Finally, we learned about different ways of using embeddings in our network. The first was to learn embeddings from scratch as part of training our network. The second was to import embedding weights from pre-trained word2vec and GloVe models into our networks and fine-tune them as we train the network. The third was to use these pre-trained weights as is in our downstream applications.
In the next chapter, we will learn about recurrent neural networks, a class of network that is optimized for handling...