03/04/2026 5:10 am
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Is linear algebra more important than calculus for machine learning because I am honestly about to lose it with these multivariable derivatives and it is making me want to quit the whole thing. I have been trying to self-study for about three months now while working a full-time retail job here in Seattle and I only have until the end of next month to finish this specific data science certificate before the discount voucher expires. I feel like I'm drowning in partial derivatives and chain rules and honestly it feels like such a slog that isn't even clicking. Every time I open a textbook or a video it is just pages and pages of calc but then when I look at actual code or libraries like NumPy it is all matrices and vectors and dot products.
I am getting so frustrated because I spent nearly 200 bucks on this specialized math for ML course and I feel like I am learning things I might never actually use in a real job. Like do I really need to know how to calculate a Hessian by hand or spend hours on these complex optimization problems manually? I was talking to a developer friend the other day and he said he basically only uses linear algebra for everything related to tensors and data shapes and barely ever touches the heavy calc stuff anymore since the libraries handle the gradients anyway. Now I am just confused and annoyed because I feel like I am wasting my very limited study time on the wrong thing and my brain is just totally fried after an 8 hour shift.
If I just focus on getting really good at matrix multiplication and eigenvalues and understanding dimensions can I skim the heavy calculus stuff or am I just setting myself up for failure later on? I really need to know if I can pivot my focus because this current path is just making me miserable and I dont want to waste my money or my time if the priorities are shifted in the real world. Is one actually more essential for day to day work...
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03/04/2026 5:45 am
Regarding what #1 said about "Your friend is totally spot on...", I had that same epiphany after burning out. In my experience, calc concepts matter for understanding training, but lin alg is what you actually code daily. My focus shifted to:
- Tensor dimensions
- Cambridge University Press Mathematics for Machine Learning Hardcover Basically skimmed the heavy proofs and honestly, I havent looked back since.
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03/04/2026 5:55 am
Just catching up on this thread. In my experience over the years, I have tried many different learning paths, and honestly, the math-heavy route is a total trap for most people working full-time. You are gonna burn out way before you get to the fun stuff. I would compare two specific approaches here:
- The Applied Method: Grab a copy of O'Reilly Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd Edition. It is far more cost-effective than those specialized math courses and shows how the logic actually maps to code.
- The Visual Intuition Method: Use free resources to get the concept of what a derivative represents. If you understand that a gradient just points the way to a minimum, you can let the software handle the actual numbers. Basically, skip the manual Hessians. If you know how matrix dimensions line up, you are 90 percent there for day-to-day work. Save your mental energy for understanding data shapes... that is where the real bugs live anyway.
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03/04/2026 5:10 am
Your friend is totally spot on and I love that perspective! Honestly, focusing on linear algebra is a total game changer for your sanity. I went through the same thing and realized that being solid with matrices is what actually keeps your projects safe and running. It is the most reliable way to understand what is happening under the hood without losing your mind!
- Most modern tools use auto-differentiation so you basically wont ever calculate a Hessian by hand in a real job.
- Understanding dimensions and tensor operations is what actually stops your code from throwing errors every five seconds. Seriously, prioritize the matrix stuff! It makes the whole process feel so much more amazing once you stop drowning in calc.
