I learn the formal material according to necessity, i go straight into whatever it is i want (learning algorithms for example) and when i need to study something else, i go and study that thing. That way i don’t spend months studying irrelevant material only to find out i didn’t need it.
Have you gone over the low level stats stuff or how to use the popular libraries out there now?
You mean in a formal way? If so, no, if i have a project i just learn enough to do that project or subject.
I’ve used a few libraries/frameworks but i’m probably not proficient in any of them.
I am approaching my final semester before I get my BS in CS and I was debating between AI/machine learning or a high performance computing course. I am worried that if I miss this opportunity I wont be able to get a concrete foundation if I ever wanted to get into data science.
That’s something only you can judge though, each person learns in a very specific way. Financially, data science is almost certainly a good choice.
If you like it, go for it man.
I also like C++ and C and I think I will like high performance computing. I also dont want to be in college later than I have to. I want my cake and eat it too.
If I could be certain to take a data science course online for after graduation, to start from the bottom to go up I would just plan on taking that.
1 Like
Dam RH Satellite is pissing me off agh :?
/end rant
All is well, i just had to make a whole new service on another computer to make it work! but to be fair, im not exactly doing it they way it should be done.
Anyone ever done IPv6 / DNS auto-configuration?
2 Likes
Does anyone know kind of hardware is usually used for learning the functioning/reverse engineering of a basic BIOS, something fairly old IBM etc. stuff or is there some sort of a modern board well suited for this purpose with a open or just a relatively simple implementation?
You can look at OpenBMC. There are some vendors that support this. Intel pledged to start supporting this more.
"debating between AI/machine learning or a high performance computing "
Just my perspective as an EE who writes code for a living - and has for many years:
-
AI/ML is a an important, perhaps dominant in terms of “new development” sub-set of HPC. HPC nodes are now routinely deployed as heterogeneous nodes of CPU and GPU with ever more being devoted to GPU. It’s become inescapable, even where it perhaps should not be applied
-
Your precise course selection will not likely be a binary determining factor in your career path - that will be determined by employer choice, chance, effort, skill, talent, networking, and risk taking.
If you must choose between those two courses, personally, I’d look to the project work and/or prof for each course - which provides the best story to tell in your interviews for your first job?
I sincerely doubt which you choose will be a significant factor in employment/career, but rather your ability to show your first employers that you learned how to learn and are not starting from scratch for what they need. I will say that if you truly have a passion for any one field, you may very likely find (as many engineers have), that you have to constantly course correct toward that passion and away from more immediate application of your skills. There are many black-holes of engineering that pay well, but steer you away from the types of problems you love to solve.
This^, yes this! CS major here who has been stuck in IT for the last 15 years of my career. It am very good at IT, but as a result, I don’t spend as much time researching, developing, and problem solving on a truly mathematical level that I desire. It has become so much of a black whole that I am now drained by the end of the day and do not have the drive to pickup those projects that I love anymore. You can be too conservative with your course corrections.