Well… this is going to be weird…
I would like to build my very own version of the fastai library, though much, much, much simpler.
I see a lot of people on the forums doing really amazing things with the library itself (hello @anandsaha ) or using the library to great success in Kaggle competitions. I guess I do not know Python that well (yesterday for instance was learning about iterators) and I also seem to have a thing for simplicity - I really, really like toy examples, etc.
I basically want to take what worked best for me in studying Ruby on Rails and apply it here. Rails is now a massive framework that is really hard to navigate but there is this cool book - Rebuilding Rails - that took me from being a super novice kind of person, allowed me to touch things and play around with them, and took me to having at least a somewhat decent understanding of how the major pieces fit together. I have spent my last two years working with rails and it was only this book that made a real difference in my understanding of things.
Personally, I am starting to see it as my limitation that I am that way, that I am having a hard time using high level, complex things. I still think I have come a long way in my thinking from where I was a year ago (thx to adamantly following advice from Jeremy and Rachel) - here I do think that this approach might be justified and is something I am actually excited to do! More so than getting a superb score on this or that kaggle competition, though I am hoping to come to that at some point
Obviously, I would like to ask @jeremy if he is okay with my approach, if starting with a blank slate and moving things over as I need them would be okay with him. Ofc I have no aspirations of this ever being usable at all for anything and if I do keep it in my public github repo I will basically say that hey, go there to fastai, since this is me just messing around to learn stuff. I do also hope that at some point - through this activity - I will be finally able to make non-trivial contributions to fastai library. If I can come to the point where I understand what is going on in the code and can add functionality per Jeremy’s suggestion or based on me finding a need for something, that would be a dream come true.
Yesterday I started doing the thing Jeremy outlines in one of the ML1 lectures - start creating synthetic datasets and playing around with them and it was a great experience. I ended up fitting a sigmoid somehow to some weird two class dataset I created I think this here is just taking the concept to next level.
Also, another concept I bring from my experience on becoming a self-taught rails dev, and in the words of the creator of rails -
read a lot of code, write a lot of code
When I open a jupyter notebook and I embark to do even simple things, I encounter a lot of friction and need to be looking up even simple stuff continually. I think that this exercise would finally allow me an opportunity to maybe reduce that a little bit by reading a lot of code and writing a lot of code
@jeremy I do realize that this might be a bit unusual and would really love to hear what you think - above all wouldn’t want to do anything that you would find questionable or not in the spirit of how you envision learning or even use the fastai library in a way that you would not like it to be used.