I didn't do these particular courses but I found it a lot easier to stay motivated with the top down approach. First demonstrate usefulness, then deepen fundamentals.

When I was younger and didn't work full time + have other commitments the bottoms up approach appealed to me more, I think partially because I had bigger time blocks to allocate. i.e I could spend a whole weekend just learning fundamentals of some particular thing I was interested in and reach the first levels of usefulness in that one "session".

These days smaller time blocks mean that I need to walk away with something the keep the spark going for most curiosities.

I did (old versions of) both of these and liked both. What I liked about the top-down approach of fast.ai is that it worked the way I approach working with other programming systems. You have a thing you want to do and APIs that promise to do that thing for you, and you plug them together. Then you decide you want to change it from the default behavior, so you tweak the parameters, then you need to learn why they're set up the way they are, and how they work, etc.

Similarly, when I learned web development with Rails over a decade ago, I didn't start by building an HTTP stack. I started by doing the build-a-blog-in-fifteen-minutes tutorial. Now I had a working project. Eventually I needed to learn all of the underlying technologies, but it's much easier and more rewarding to have something running first.

I found that starting with the big picture and a tangible result made it easier to stay engaged. At the end of the fastai course, however, I felt there were some gaps in my understanding especially at the low-level side. Andrew Ng's course helped fill in those gaps.