I have taken all of the fast.ai courses available and they have been life-changing for me. The style of teaching that Jeremy uses really clicks with my learning style (Code then explain) and I have gone from knowing nothing about machine learning to finishing pretty highly on a few different competitions mostly involving NLP and Computer vision. I have been working on Super Resolution more recently and I started working as a Data Scientist at work which has been great. I actually didn't even have a ton of programming experience when I started fastai 3 years ago, but now I am pretty confident when I need to code something. I feel like I have moved through the stages from script-kiddie of data science to a point where I can actually intuitively understand what is happening and trust my instincts to change well-established architectures in ways that it works better for the problem that I am focusing on.
I was lucky enough to participate in the lectures earlier this year. I've watched every fastai course before this and I will continue to watch them, even the introductory material.
That being said, this course is completely different than anything done before. This course takes a look at the very most basic functionality of neural network libraries and proceeds to implement the fastai library totally from scratch. So it's really a great deep learning + tooling + api design + programming language + research implementation course all in one.
The last two lectures on Swift were especially fun. Python isn't great for deep learning because you always have to live at arms length from the implementation through a c++ wrapper. Swift can compile straight to the low level system commands. LLVM, MLIR. Still learning this so I'm not the best person to answer questions about this specifically. :)
So Swift has this awesome feature set of being well designed, pretty similar to Python, statically typed, and low level. These lectures are hosted by Jeremy Howard and Chris Lattner. Sometimes the most valuable learning I can do is to just listen to the banter between two exceptionally experienced experts, so this make the course that much more enjoyable.
I started using the techniques immediately, for applicable deep learning and NLP projects. There's no need to finish the entire course to get some actionable rapid prototyping done. Each lecture is nearly self contained once you're comfortable with the basics.
The content is very well paced and engaging. The use of Jupyter Notebook really is a great way to demo code. I took Part 1 online (free) and then Part 2 in person ($2000) and feel the money was well spent. TBH, I haven't used it yet, but I enjoyed the classes (they were exciting at times) and I do have a project I'm working towards where I'll use this.
I've gone through all previous iterations of their courses, so have familiarity with using pytorch and the fastai library, but haven't done the best at keeping up with their rapid pace. This course helped me understand the building blocks of the library, which in turn helped me out in a few projects.
For one project, I adopted what Jeremy taught about building code for processes, which our team has then used for a couple other things as well. For another project, I was able to easily put together the code for training on a set of data with a somewhat complex structure.