[dgb23]

This article is way over my head right now. But I bookmarked it. Differentiable programming and probabilistic programming are among the things that motivated me to learn the language (still a beginner), aside from just brushing up and sharpening my math skills in a practical manner.

About that... One thing that I didn't expect but should have been obvious is that introductory content is often geared towards scientists/mathematicians rather than engineers, which makes sense given that this is the target audience.

They often explain the programming side and not the mathematical/scientific side. Which is fine, because they present the right vocabulary for me to explore from different sources.

This article seems to be very much engineering focused but there is a ton of vocabulary I'm not used to yet. I assume the reader is expected to have a solid understanding of the paradigm and at least a high level understanding of Zygote.

[KenoFischer]

You are correct. Our technical documentation is mostly aimed at working scientists who want to start using these techniques in their work. That does sometimes lead to funny cases where a document assumes you know what a smooth manifold is but will explain try/catch blocks. We've started trying to put together more introductory-focused material at https://juliaacademy.com/. We don't currently have anything particularly AD focused (outside of the general ML courses), but I think that's a topic that's high on the interest list.

[dgb23]

Thank you for pointing out this fantastic resource.

> mostly aimed at working scientists

My primary goals are to learn what (primarily) data-scientists do. In the sense of: How do they think and approach problems, what are the limitations and the prerequisites etc. (And as I said to improve my math skills.)

I think there is merit in engineers learning these things (within reasonable scope) because at some point there needs to be a system that provides and transforms data into a format that scientists/analysts can work with. And in the other hand there are things that engineers can implement and learn to improve their systems. I'm excited about both and curious about how far I can get.