Personally, I found it took me much longer. I watched Karpathy's lectures, took notes and stewed upon the ideas, and read a bunch of other materials such as blog posts and research papers to try and truly comprehend some of the concepts mentioned in the course.

I found myself knowing how to create CNN's, but the why of the entire process still feels under-developed. But I'll admit it could be because my understanding of Calc and Linear Algebra was far more under-developed back when I was studying the course than it is now.

What did you read/do to develop your calc and linear algebra skills? I feel like I know calc and linear algebra fairly well on paper, but I'm unsure how to translate that to the computer.

Are these courses being taught by graduate students? The three main instructors seem like they are students themselves, with an army of under and grad TAs.

Pity that you pay so much money to attend Stanford only to be taught by your peers. Not knocking on Stanford as this is how is being done much everywhere in the undergrad level now.

In the first few lectures of the course you get a pretty good history of deep learning and you'll see it didn't really take off until around ~2012. And the reasons for it taking off is mostly because people are getting better at the black magic of training a deep network.

So these grad students are exactly the people you want to learn from because they have done the dirty work of fiddling with parameters to know what tricks work and what doesn't. It's probably preferable to a more theory-heavy course because very few people (not even the more experienced professors) understand why those tricks work.

Note: I took an older version of the course which was started by Andrej Karpathy who was a grad student at the time but is now the Director of Artificial Intelligence at Tesla.