it is a practitioner-style style deep learning course that instead of starting with the fundamentals starts with examples and results and then over time, layer by layer reveals what it is all about and how it works in detail until you ask yourself "that is all there is?". a great way to make a seemingly unapproachable topic approachable.
you don't need big data, you don't need a GPU, you don't need to install a ton of dependencies, you only need a browser (to access jupyter notebooks).
last but not least: this is kind of the "definitive version" of the course as it now comes with a book, a new version of the library (re-written in a more thoughtful way) and with new versions of recorded lectures/lessons based on the book w/ way better audio quality (compared to the previous ones).
If you ever were curious about deep learning but did not find the time to take a look or thought it was unapproachable: now is a great time to dive in and this is a great course (& book & library & community) to do so
> it is a practitioner-style style deep learning course that instead of starting with the fundamentals starts with examples and results and then over time, layer by layer reveals what it is all about and how it works in detail until you ask yourself "that is all there is?". a great way to make a seemingly unapproachable topic approachable.
Well said and this is exactly what I loved about the course and the way Jeremy peeled things back. If you're a 'learn-by-tinkering' person, and I suspect a lot of HN folks are, I can't recommend it enough.
exactly! This is the reason why I recommend this course to my friends and colleagues who ask me about how to get into AI/ML. I tell them to do this course first to get idea about what all is possible. The version 2 (which you do at your own pace) provides the theory/maths behind it.
While coursera/Andrew Ng course are(were?) classic and have great content - I personally prefer Jeremy's style and this code-first approach to Deep Learning (yes, DL != ML != AI but that's not the point).
I think wadkar means part 2 - deep learning from the foundations - which you could do optionally after finishing the 'main' course.
It USED to live here: https://course.fast.ai/part2
but has disappeared, I reckon as part of the new course relaunch. Perhaps there'll be a new version of this part too?
Correct, I meant part 2 of the course and not the version 2.
Thanks for correcting me a_bonobo.
As for revision of this part 2, based on my skimming of forums.fast.ai , I don’t think it will happen anytime soon. The second part is more about theory/maths which is not affected by the new library.
Would it teach principles of deep learning in the same depth as if I slogged through the YouTube videos of cs231n?
A legitimate question as I'm considering embarking on one of these two paths. As most of the people here my programming skills are more honed than my math skills so the fastai path looks like the easier road to take but I'm not sure if they both lead to the same place.
If you get serious about data science, you're going to end up reading/watching a lot of different resources. I'm a data scientist and I'd suggest starting with fast.ai and then following your interests elsewhere. The best book or course is the one you're going to finish.
it is a practitioner-style style deep learning course that instead of starting with the fundamentals starts with examples and results and then over time, layer by layer reveals what it is all about and how it works in detail until you ask yourself "that is all there is?". a great way to make a seemingly unapproachable topic approachable.
you don't need big data, you don't need a GPU, you don't need to install a ton of dependencies, you only need a browser (to access jupyter notebooks).
last but not least: this is kind of the "definitive version" of the course as it now comes with a book, a new version of the library (re-written in a more thoughtful way) and with new versions of recorded lectures/lessons based on the book w/ way better audio quality (compared to the previous ones).
If you ever were curious about deep learning but did not find the time to take a look or thought it was unapproachable: now is a great time to dive in and this is a great course (& book & library & community) to do so