Ksenia's Public Channel on #whatever

Of course, deep learning libraries are all built on OOP because

(1) datasets, models, trainers, learning rate schedules etc etc. are indeed all real world objects
(2) they need to be reutilizable and adaptable
(3) they are super optimized as well

but it doesn’t mean you have to build every time every structure from scratch.

You build a transformer out of pure Torch when you are in the uni. And then you may build some small custom object on top of some stuff that already exists. Or you have to rewrite the cose for deployment (but machine learning engineers are a different breed). Or you are writing open source libraries.

Most of those cases are outside the scope of AI researcher. My dirty small scripts are serving me just fine.

(Ofc you have to know OOP and know how to read it and do it. But it doesn’t mean you have to.)

www.tg-me.com/us/Ksenia& 39;s Public Channel on whatever/com.ksenia_and_ai/525

56 viewsJun 26, 2023 at 14:58

Of course, deep learning libraries are all built on OOP because

(1) datasets, models, trainers, learning rate schedules etc etc. are indeed all real world objects
(2) they need to be reutilizable and adaptable
(3) they are super optimized as well

but it doesn’t mean you have to build every time every structure from scratch.

You build a transformer out of pure Torch when you are in the uni. And then you may build some small custom object on top of some stuff that already exists. Or you have to rewrite the cose for deployment (but machine learning engineers are a different breed). Or you are writing open source libraries.

Most of those cases are outside the scope of AI researcher. My dirty small scripts are serving me just fine.

(Ofc you have to know OOP and know how to read it and do it. But it doesn’t mean you have to.)

BY Ksenia's Public Channel on #whatever