My take on these is adding somethings I picked up along the way to add another perspective and some additional info.
Any feedback on using it?
How is that learning how to build something, as opposed to simply using a system someone else built?
See this paragraph in the article: https://www.zerotosingularity.com/blog/fast-ai-part-1-course...
Fast.ai uses a top-down approach, allowing students to get awesome results with a minimal set of instructions, and start to dig deeper from there. If you look at the takeaways from the first lesson, you can see it's more than just three lines of Python and you're done, It's a solid and fresh approach to learn some actual practical deep learning.
Additionally, it kind of depends on what you want to do, we are all using things other people built, depending on the level of abstraction we care for or are willing to deal with...
At the moment, I'm learning fast.ai/PyTorch in parallel with Keras/Tensorflow, so at this point, I have no definitive answer to your question which one is preferable. It will probably depend and they will most likely have their own benefits (I know that the boring answer, but I need to get more experience to give you a better answer).
As an exercise I'm trying to write the fast.ai notebooks in Keras, to see how they stack up. Might need to do a post on that as well.
I hope to answer your question better in the future. Could you tell me more about what you want to achieve, I might be of more assistance?