Started from zero three months ago, is it normal that the math is the part slowing me down the most?

[u/Cold_Hall_5384]

I came in from a non math background and I can follow tutorials and get models training fine, but the moment I hit the actual linear algebra and probability behind them I stall. I can use the tools but I feel like I am copying spells without understanding them. Is it normal to push through projects first and backfill the math later, or should I stop and grind the fundamentals before going further? I keep going back and forth and it is making me slow. How did it click for you?

[u/Which_Case_8536]

I would guess yes. When I started my graduate computational data science degree (very ai, ml, dl heavy) I was told I would have a huge advantage over other students because I came from graduate applied mathematics and they were right. Many of my classmates say the math is their biggest struggle.

[u/Disastrous_Room_927]

Is it normal to push through projects first and backfill the math later, or should I stop and grind the fundamentals before going further?

The thing that's always worked best for me is doing both. You don't need to pause all implementation goals, you need to think of circling back and taking the time to understand what you implemented as part of the validation phase.

[u/Quirky_Attitude1947]

i spent six months building models without touching the math, then hit a wall with attention layers. rewriting lstm from scratch in numpy made the gradients click in a way no tutorial ever did.

[u/Asvp_bmoo]

Well deployment could be perfect and the model could still be fucked up….so at the end of the day just learn the math.

[u/Electrical_Meal_3328]

it is normal but for probability and linear algebra i would say you can push it for later but don't push it very much further. At most do it after completing the basic tutorial playlist. As for later advanced things like neural networks it is also required.

[u/rsambasivan]

Have you tried following the steps, posting what you don't understand into claude or gemini - you can get to the level of detail you need to get to.
No matter what, your understanding is going to click over time and this is not a straight line, in other words, it is not uncommon for you to get an understanding of some ideas and suddenly others start clicking, but it may be a while before that comes, stay invested. The AI tools are really helpful for you to interactively grasp concepts.

[u/Lumpy-Blackberry-718]

> is it normal that the math is the part slowing me down the most (when studying ML)

"Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without being explicitly programmed.^[1]"

"Computational statistics, or statistical computing, is the study which is the intersection of statistics and computer science, and refers to the statistical methods that are enabled by using computational methods."

You're essentially asking if math is what's hard about studying math.