So in this lecture, we'll be summarizing what we learned in this section.
This section introduced feedforward artificial neural networks.
Although there was quite a bit of theory involved, the code was quite simple compared to what you already
learned.
Essentially, all you had to do was add another dense layer.
However, this was only for the easy case where inputs were still represented using TFI Taf.
Shortly thereafter, we learned how to prepare this text with Keros, which automatically does a lot
of the same work we previously discussed.
Importantly, we also learned about embeddings, which allow us to create a more granular representation
of text in particular, instead of a whole document becoming a single vector.
We map each token to a vector individually.
Now for this section, we don't yet know what to do with all of these vectors, but we'll see how they
can be handled with CNN's internet.
What we did learn about was how embeddings could be used in an ends by taking the average of each individual
word vector.
In fact, this is what we use in the C bao variant of words of EC.
In this section, we got to implement this version of words of EC, and we saw that it produces results
similar to what we saw in our neural word embeddings demo earlier in the course.