Is data that important?

Data is the lifeblood of ML algorithms. Your models will only be as good as the data you provide to them. After all, you are what you eat. We have to focus on developing a good, clean dataset for learning. This begins with getting an environment set up and preparing the data to be ingested into an algorithm. We do have a fundamental advantage within this process because GANs can take considerably smaller sets of data than other techniques. This advantage comes with the explicit caveat that we will need to ensure that the data we're using encompasses the entire trade space of possibilities for our application.

Getting ready

One of the deep dark secrets they don't teach you about this field is that you're going to spend a large chunk of your time preparing the data (sometimes as much as 75% of a project). I've had people ask me over the years why data preparation can absorb so much time and the answer really is simple:

Garbage in -> Garbage out

Data will drive your project...