I've had a similar idea on my backburner for two years, as a fun project to dabble in Elixir, but this is just so much more _fun_ than my version which looks like an admin page that just happens to have a Vim canvas on it.
In hindsight, yes that was a bad move (especially considering that my work laptop is still locked to my banned ID…)
As an Apple noob at the time, I assumed that if my MDM-managed device prompted me to log in with my Apple ID, that it of course would be an allowed action.
With regards to data being shared, the only thing I noticed was wifi passwords and peripherals pairing (apple keyboard).
With risk of being spammy, this is probably the most relevant discussion I've seen so far on HN w.r.t my experience of being locked out from my Apple ID.
I hope legislation will force Apple to step up and be more transparent / helpful.
True, but Apple specifically in my case was abhorrently indifferent to the consequences and their support structure is entirely unable to help you. If you have a dedicated storage provider, you lose their single service only. With Apple, you lose everything you have attached to your account which carries a much higher risk.
Read Deep Learning with Python [1]. It's ok not to understand everything, but doing the labs will be invaluable.
That book should set you up with the fundamentals. Pytorch is the defacto standard right now for training; during deployment you will use whatever your deployment setup allows (e.g. embedded devices typically have their own inference frameworks).
Python is the lingua franca for anything in machine learning, but other languages are used where necessary for performance or for ecosystem benefits.
I'd recommend moving to development that interfaces with ML instead of moving to being an ML practitioner. You have much faster feedback cycles, your work is predictable (engineering, not science), and you don't feel the pressure of never reading enough papers.
To do that you only need to understand the fundamentals of tensors, some basic knowledge on what the big no-nos are within ML development so you can course correct your peers if they break them, and either focus on the operations side of things or deployment. In both cases, having a knack for optimizing bottlenecks will be very helpful since they will be present during both training and inference.
I've never found this style readable unless with pipes (bash / elixir), where I love it. With any other syntax, I find it just adds mental overhead. Maybe because you have to read it backwards?
https://skogsbrus.xyz/dont-put-all-your-apples-in-one-basket...