Since I have presented two viable alternatives for document classifications, this chapter will contain two separate examples for document classification. Both will use embedding layers. One will use an LSTM and the other will use a CNN.
We will also compare the performance between learning an embedding layer and, starting with someone else's weights, applying a transfer learning approach.
The code for both of these examples can be found in the Chapter10 folder in the book's Git repo. Some of the data and the GloVe vectors will need to be downloaded separately. Instructions to do so exist in comments within the code.
