I don't have the time to immediately work on this course, but I might later this year.
What I'd like to know, as someone who has completed (in the past) the following MOOCs:
1. ML Class (plus about half of AI Class) - 2011
2. Udacity's CS373 Course - 2012
3. Udacity's Self-Driving Car Engineer Nanodegree - 2016/17
...and gained a lot of knowledge about ML and DL thru them - would this fast.ai course be worthwhile to pursue as an addition to my prior education on the subjects? Would I find it just as challenging (from a cursory glance it looks to be a "yes", but I wanted another opinion)?
I'm open to suggestions from everyone here, not just jph00/fast.ai ...
Also - and this I would also like opinions on - would my time be better spent pursuing other areas I know I am not proficient in - mainly learning about probability/stats, as well as basics in calculus...
For instance, in the prior courses, there was always mention of - for say backprop - stuff about the "chain rule" and other "rules" for calculus, and I feel I need to understand that more. That is, I am wanting to understand things in ML and DL at a lower level; how the "black boxes" really work. So would I be better off pursuing that, or just continuing with courses like this one?
Or maybe both at the same time (if that is even possible)?
I've also considered that I might need to take those subjects (probability/stats/calc) as actual courses via community college or similar (or online MOOCs).
Whilst it's absolutely not required knowledge for most deep learning, if you are interested in more fully understanding the chain rule, my friend Terence Parr (the ANTLR guy) and I wrote a fairly thorough tutorial for matrix calculus https://explained.ai/matrix-calculus/index.html .
Ive watched the first two episodes of fast ai and one thing i can say is that the whole approach of this course is the opposite of what the courses you listed are structured like. Fast Ai jumps right into "how to use the script/tools" while slowly teaching the concepts behind why they work in comparison to the typical ML/AI course that involves complex learning calculus and statistics before doing anything practical.
SDC alumni here as well with a couple of ML courses under my belt as well. Did any of these courses help you advance or pivot your career? I'm still struggling and looking for advice how to move forward into AI/ML (I have a Master in Mech as well). Any advice will be highly appreciated!
What I'd like to know, as someone who has completed (in the past) the following MOOCs:
1. ML Class (plus about half of AI Class) - 2011 2. Udacity's CS373 Course - 2012 3. Udacity's Self-Driving Car Engineer Nanodegree - 2016/17
...and gained a lot of knowledge about ML and DL thru them - would this fast.ai course be worthwhile to pursue as an addition to my prior education on the subjects? Would I find it just as challenging (from a cursory glance it looks to be a "yes", but I wanted another opinion)?
I'm open to suggestions from everyone here, not just jph00/fast.ai ...
Also - and this I would also like opinions on - would my time be better spent pursuing other areas I know I am not proficient in - mainly learning about probability/stats, as well as basics in calculus...
For instance, in the prior courses, there was always mention of - for say backprop - stuff about the "chain rule" and other "rules" for calculus, and I feel I need to understand that more. That is, I am wanting to understand things in ML and DL at a lower level; how the "black boxes" really work. So would I be better off pursuing that, or just continuing with courses like this one?
Or maybe both at the same time (if that is even possible)?
I've also considered that I might need to take those subjects (probability/stats/calc) as actual courses via community college or similar (or online MOOCs).
Any thoughts or suggestions are welcome!