The value of PyTorch lies more in utilizing accelerators like GPUs while offering a nice abstraction. But you can build your own (inefficient) tensor library without too much effort as e.g. Andrej Karpathy has shown in his "NN zero to hero" youtube series.
Even if most people had calculus in high school (which is not a given), I'd expect pretty much anyone who did not go into STEM to promptly forget it, just like I forgot much from chemistry and biology after studying Computer Science.
I have to imagine they just asked Claude to dive into a topic and generate example problems along the way.
As for the content... a lot feels like knowledge overload and concepts are introduced without explanation or "why". It basically says "here is a training loop" and never answers my immediate question of "...for what?" It also introduces random concepts like setting the seed that don't look like they're even in the final training loop it provides.
As a comparison, Pytorch itself has docs that go over the entire training loop as well. And it explains why you're loading the data its loading. With examples that run instead of being scattered on.
I also put the content of a workshop into an LLM detector and it said 100% of the text was likely AI generated.
I'd just like the author to disclose how much of the content is entirely LLM-generated.
Do you disagree?
Also OP didn’t even praise the content. They said it “seems interesting.”
Note that I can't reproduce it myself on this submission, but I've seen it so many times and in so many different places that I don't think it's a website issue.