stefr-
e: from the manpage:
"podman compose is a thin wrapper around an external compose provider such as docker-compose or podman-compose. This means that podman compose is executing another tool that implements the compose functionality but sets up the environment in a way to let the compose provider communicate transparently with the local Podman socket. The specified options as well as the command and argument are passed directly to the compose provider."
This resonates with me, I was in exactly the same position when I needed to do something with `kubectl` JSON output - just ask ChatGPT because I couldn't be bothered to learn the unintuitive syntax.
Interestingly I _can_ blame the tool, because I started using Nushell[1] which has built-in JSON manipulation that provides a MUCH simpler syntax, and I have learnt this properly because it was that easy.
I like this approach because it allows me to connect the math to the problem, whereas otherwise you wouldn't have.
In the book, you're slowly creating a DL framework, as the title says, from scratch. He also has all the code on GitHub: https://github.com/SethHWeidman/DLFS_code
I think if you are truly trying to understand deep learning, you will never get to avoid the math because that's really what it is at it's core, a couple of (non-linear) functions chained together (obvious gross oversimplification).