I think all this learning methodology is really important… and I think the value of learning to learn is a super valuable ability one acquires while doing the fastai curriculum… but was also thinking that maybe some people a little bit newer to the lectures or who have not experienced being part of this sort of environment might be feeling a little bit lost…
By all means nothing can substitute redoing lecture notebooks and that needs to be the first stop… but if someone would want to venture of the beaten track a little bit more, I think the datasets by fastai are a really underappreciated asset. Such a variety there and they are the perfect size for the the hw currently available to a majority of students.
I think those datasets are such a cool initiative on so many levels. I started working on doing something I find quite exciting with the imagenette woof, but thought of keeping the repo private till I am ready to share it… but now I think maybe someone could use a bit of showing the ropes how to get the data, etc, so making it public right now…
Here it is https://github.com/radekosmulski/woof 
It’s all you need to get started with training a custom architecture. There are so many things that could be done with it. You could try to beat Jeremy on the leaderboard. A bit of a challenge there, sort of like a competition mentioned above. But at 40 epochs with 160px this is great as training shouldn’t take that long and would be a great opportunity to try things out.
Not even a readme there yet, but everything you need (pulling data, etc) can be done through running the available notebooks. The notebook with training contains also some overall information on some good practices which maybe also can be interesting.
Hoping to add more to the repo hence have not been planning on making it public just yet, seems there might be some use for this already Might be a nice way to brush up before part 2.
For ideas and as a reference point, here is a repo I started working on with mini projects after part1 of v2 finished. This is using fastai v0.7 mostly I think, but maybe can help someone with ideas on what to do as an exercise.