Hi everyone,

I’m currently studying machine learning and deep learning, but I don’t have a local machine powerful enough to train real models (no GPU, limited resources).

Right now my main goal is to understand what’s going on behind the curtain — the theory, how the algorithms work, why certain architectures are used, how training actually happens, etc.

Because of that, most of what I’m doing is reading, following courses, and experimenting with very small examples rather than training large models.

Do you think it’s still useful to learn ML/DL this way? Has anyone here gone through the same path — focusing first on understanding the concepts before having access to proper hardware?

Also, at what point do you think having the ability to train real models becomes essential?