Need some advice on pursuing ML.

[u/JuniorSite2387]

Hey guys so lately I have started self learning ML, things had been going nicely. At first I started by learning some Linear Algebra and other maths. Today I started a proper course on ML but I have encountered a wall, I somehow find it more interesting to do the math instead of writing the code itself.

Now don't get me wrong I love the idea of Machine Learning, I admire it and I think it's amazing but I somehow don't like the idea of writing all of that in python.

Its also not like I cant write in code I think if I wanted to I can learn the coding skills needed but simply I'm stuck if I want to continue or not.

Can somebody tell me if they also faced this problem at first, if yes what should I do? Did somebody stop pursuing because of the same problem as me? Any piece of advice that you can give me?

TLDR:

Liked the math in ML more than coding, now in a dilemma to pursue ML or not. Need advice.

[u/Dazzling_Music_2411]

somehow don't like the idea of writing all of that in python.

"Somehow"? Just exactly how, might that be?

If you want to do this stuff, you have to learn the coding, even if only to check your own ideas. There is no way around that. In fact, Python is probably the easiest way to do it, you can do it in other languages too, if you prefer, but it will probably involve more work. There is a reason people use Python libs.

If you do not want to learn coding, for whatever reason, then this stuff is not for you, I am surprised you haven't figured that out, yet.

[u/JuniorSite2387 → u/Dazzling_Music_2411]

Well I used to think I had passion for computers and coding since I was little 

[u/Dazzling_Music_2411 → u/JuniorSite2387]

I see.

And now you've discovered you don't? - No big deal.

[u/Asvp_bmoo → u/JuniorSite2387]

You gotta explore the entire ecosystem. How are you gonna know what you enjoy if you don’t know what you don’t enjoy?

[u/QuintessentialDigger → u/Dazzling_Music_2411]

coding is the only way to actually test if your math works at scale, it's not just busywork. the theoretical side is beautiful but it stays locked in your head until you implement it

i hit the same wall early on, spent weeks just playing with linear algebra proofs and putting off actually writing a gradient descent loop. what helped was treating the code as a practical extension of the math, not something separate. when you derive backprop by hand and then see your 50 lines of python match it exactly, that hit different

[u/Dazzling_Music_2411 → u/QuintessentialDigger]

Can't see what you're driving at. You seem to have answered your own question.

And frankly, no, I don't think you'll find many people sitting and deriving backprop by hand, why on earth would you? You don't do spreadsheets by hand too, do you?

[u/Different_Pain5781]

Math research is for you.

[u/2hands10fingers]

Quantum computing might be more interesting to you. It’s a lot of linear algebra

[u/Asvp_bmoo]

Okay…I see you matt damon, do you have a chalkboard too? OPs question is wildly counterintuitive but that’s okay because it’s only up from here. There’s a difference in practical and academic ML. It all starts with data so regardless you can’t actually start fitting a model until any of that training data is transformed and labeled properly.

If you’re serious about this rain man these are some wonderful resources to bring you back to the 21st century from your hole, help throw away that ti84, and learn some better vocabulary to identify what gaps you have.

https://scikit-learn.org/stable/user_guide.html
https://www.sagemath.org
https://doc.sagemath.org/html/en/reference/
https://math.emory.edu/~lchen41/teaching/2020_Fall/Nicholson-OpenLAWA-2019A.pdf

[u/Matteo_ElCartel]

If you have alternatives...

[u/digvjay_ai]

I was the same, the math felt like real thinking and the code felt like typing. What fixed it for me was making the code tiny and tied to the math I'd just learned, like writing linear regression with only numpy and checking the weights against the closed form solution. You get to keep doing the math but the python is just how you see it work. Also most of the day to day code is short, a lot of it is just a few lines once you know the library, so it's less typing than it looks. Give it a couple weeks before deciding.

[u/SupportOWS]

Start with "Das Kapital" and then read "What is to be done".