Now is the time to experiment with a few NLP techniques with the help of PyTorch. This will be more useful for those of you who haven't written code in any deep learning framework before, but who may have better understanding of NLP core problems and algorithms.

In this chapter, we will look into simple examples with small dimensions, so that we can see how the weight of the layers changes as the network is training. You can try out your own model once you understand the network and how it works.

Before working on any NLP-based problems, we need to understand the basic building blocks on deep learning, including affine maps, non-linearities, and objective functions.