The topics in this chapter are going to be the beginning of our journey into the different processing techniques for deep learning data processing. A large part of the job of deep learning practitioners is the data parsing part of the job. We need to take it seriously in order to ensure our models are learning from the right data. Throughout this book, you'll understand that the data parsing is a critical component of getting GANs to converge and to get decent results out of these models. Architecture is far from the only thing that can affect our learners.

There's an expansive set of examples that you will be exposed to during the course of this chapter. The key reading in the beginning is to understand the tools you'll need to run the code with this Docker overview found here: https://docs.docker.com/engine/docker-overview/.

We also recommend you read up on Python, NumPy, and SciPy:

• Python: https://wiki.python.org/moin/BeginnersGuide/Overview
• NumPy: https://docs.scipy.org/doc/numpy/
• SciPy: https://www.scipy.org/getting-started.html

I will emphasize here that the expectation is that you have a baseline understanding of these techniques. These links are meant strictly as a reference.