This was our first brush with deep learning for NLP. This was very a thorough introduction to torchtext and how we can leverage it with Pytorch. We also got a very broad view of deep learning as a puzzle of only two or three broad pieces: the model, the optimizer, and the loss functions. This is true irrespective of what framework or dataset you use.
We did skimp a bit on the model architecture explanation in the interest of keeping this short. We will avoid using concepts that have not been explained here in other sections.
When we are working with modern ensembling methods, we don't always know how a particular prediction is being made. That's a black box to us, in the same sense that all deep learning model predictions are a black box.
In the next chapter, we will look at some tools and techniques that will help us look into these boxes – at least a...