머신러닝 교육에서 수학의 중요성

### 머신 러닝에서 수학의 중요성
* 📚 머신 러닝에서 성공하려면 워크샵이나 강좌에서 다루지 않더라도 **기본 수학을 배우는 것**이 중요합니다.
* 🔍 머신러닝 기술과 알고리즘을 효과적으로 적용하려면 **선형 대수학, 미적분, 통계**와 같은 개념을 이해하는 것이 필수적입니다.
* 💡 많은 학습자가 수학에 대한 배경 지식 없이도 머신러닝을 구현할 수 있지만, **수학을 알면 모델 문제를 해결하고 최적화하는 능력**이 크게 향상될 수 있습니다.
* ⚠ 워크샵과 온라인 강좌에서 수학적 세부 사항을 건너뛰는 경우가 많아 **오해와 모델 성능 저하**로 이어질 수 있습니다.
* 🧠 과적합과 같은 함정을 피하고 정확한 모델 평가 및 개선을 보장하려면 **수학적 원리에 대한 확실한 이해**가 필요합니다.
* ✏ 발표자는 머신러닝의 수학에 초점을 맞춘 더 많은 리소스에 대한 열망을 표명하며 **현재 교육 제공의 격차**를 지적했습니다.
* 💬 시청자들은 **머신러닝의 이면에 있는 수학**을 학습한 경험을 공유하고 자신의 여정에서 이러한 지식의 중요성을 생각해 볼 수 있습니다.
* 🔗 연사는 프로그래밍 도구가 구현을 단순화할 수 있지만, **머신러닝 알고리즘을 더 깊이 이해하려면 수학을 이해하는 것이 중요**하다고 강조합니다.

수학은 머신러닝을 마스터하는 데 매우 중요하며, 알고리즘을 더 깊이 이해하고 효과적으로 적용할 수 있게 해줍니다.

How good do you really need to be in math to learn machine learning? Very good, but not in everything. Can you get away without learning math in machine learning? Absolutely yes, and that's why it's dangerously dangerous. I don't know about you, but how many people do you really see out there teaching math for machine learning? It could be some YouTube videos, or it could be an online course, or a workshop. I know a lot of workshops skip over math, and that's not good. Some of them say that it's, you know, I'm constrained, it's beyond the scope of this course, or whatever. Like, it's almost like there's always an excuse to skip over math, but math is the fundamental thing that drives machine learning, whatever algorithm you pick. So, let me know in the comments below. When you took a machine learning course, did they teach you the math behind the algorithms? CU, I'm very interested to know how many people know the math involved in a machine learning algorithm. Let it be linear regression, or you know, simple neuron networks, whatever it is. Like, how

many people really know the intricate math details for that algorithm, and this is why math is very important. Let me tell you, you know, if you're learning machine learning or you just started learning machine learning, just be aware that you can do machine learning without knowing the math, but it's very important that you know what the math behind all of this machine learning is. Look, machine learning involves a lot of linear algebra, a little bit of calculus, and quite a lot of Statistics. So, these are fundamental concepts in math that you need to know before you actually become a machine learning engineer or even a data scientist. Like, statistics is a must, right? Linear algebra is a must if you're using something like neural networks or linear regression, logistic regression, whatever it may be. And here's why math is really important, because a lot of times what I see is people applying machine learning for anything and everything, and a lot of times they have really high accuracies or very low accuracies, and that's a problem because you don't want models that overfit on

your training data. At the same time, if you want to improve the performance of your model, you need to know exactly what the problem is. And this is why I think there are a lot of people out there doing machine learning without really knowing the math behind these algorithms. And it's not entirely their fault because, you know, you take a workshop, for example, or an online course, and they skip over the details of the math. They say, Oh, okay, we get these equations. These are called, you know, activation functions or, you know, some kind of a loss function that needs to be determined. Basically, do this by, you know, following some kind of a math, linear algebra, blah, blah, blah. And then they say, Oh, we got to update the weights, so we do something called backpropagation, which involves calculus. So this is not a calculus class. Let's just skip over it, you know, spend some time on your own, learn on your own, and they'll be done with it. That is okay. It's not on them to entirely teach you the math behind machine learning. It's your

responsibility, and I know that there are a lot of people who teach ML, know linear algebra, they know how the math behind these algorithms work, but when it comes to teaching, they know that it's a lot more effort that they need to put in to refresh their memory or refresh how things work on paper at the fundamental level. So, I don't blame them entirely. Let me know in the comments below if you're interested to learn the mathematics involved in machine learning. If that's the case, if I get a good enough response, I'll probably make videos about the fundamental mathematics involved in building a machine learning model. I really wish teaching machine learning started with teaching the mathematics behind it because you can get really quick and up to speed when it comes to the programming aspect because it's not really that hard considering the fact that you've got packages like PyIT learn, PyTorch, TensorFlow, blah blah blah, but the mathematics is a problem because it takes time to sit down and, you know, understand the concepts. How do you do matrix multiplication? How do you use

linear algebra to I don't know find the determinant or something like how does gradient descent work and how do you update the weights back propagation blah blah blah what's the real purpose of the activation function and so on and so forth so my message for this video is just two things number one start learning the mathematics behind machine learning even if you're not taught your Workshop does not teach you or you've never learned the math you just know some of the basics and very broad overview of it pick up a textbook and start learning right away because that could be the make or break decision if you're looking for a job in machine learning or if you're trying to complete a project successfully and number two I want to say is don't just rely on online courses and workshops you have to go beyond that you have to go beyond just these workshops because they teach you a lot of things but there's also a lot of things that they cannot teach you simply because either they're constrained by time or nobody wants to

do the hard work of actually teaching the most difficult part of machine learning, which is the mathematics behind it. So, whatever the reason may be, I want to encourage you to go beyond just learning the basics of how to apply machine learning. Go dig up the code, the very fundamental source code, or pick up a textbook and learn the math that is being applied, because that is very important. Let me know in the comments below what you think. How important is math in machine learning? How many of you were actually taught the mathematics behind machine learning? Were you taught machine learning with math? Like, how did you guys start? Like, did you start with coding, or did you start with the mathematics behind machine learning? Let me know in the comments below and subscribe to the channel as always, and hit the like button because it goes a long way to support this channel. So, thank you all for your support so far. I will see you in the next one.

Machine learning and AI is extremely easy if you learn the math: My rant.

ChemCoder

머신러닝 학습에서 수학의 중요성

수학은 머신러닝을 이해하는 데 필수적이지만, 교육 과정이나 워크샵에서 간과되는 경우가 많습니다.

많은 프로그램에서 알고리즘의 이면에 있는 수학을 가르치는 것을 건너뛰는데, 이는 해로울 수 있습니다.

발표자는 얼마나 많은 사람들이 머신러닝 알고리즘과 관련된 수학을 배웠는지 알고 싶어합니다.

수학은 선형 대수, 미적분, 통계 등의 핵심 개념과 함께 머신러닝을 이해하는 데 매우 중요합니다.

머신러닝 엔지니어나 데이터 과학자와 같은 직무를 수행하려면 이러한 영역에 대한 탄탄한 기초가 필수적입니다.

저자는 과적합과 같은 문제를 피하고 모델 정확도를 향상시키기 위해 알고리즘 이면의 수학을 더 깊이 이해할 필요가 있다고 강조합니다.

머신 러닝에서 수학 이해의 중요성

많은 사람들이 기초 수학을 제대로 이해하지 못한 채 머신 러닝에 참여하는데, 이는 워크숍이나 온라인 강좌에서 중요한 세부 사항을 건너뛰는 경우가 많기 때문입니다.

이러한 강좌는 활성화 함수 및 역전파와 같은 개념에 대한 기본적인 개요를 제공하지만 심층적인 설명을 제공하지 않을 수 있습니다.

수학을 가르치는 것은 전적으로 강사의 책임이 아니지만, 학습자는 스스로 주도적으로 공부할 것을 권장합니다.

사전 지식이나 정규 교육에 관계없이 머신러닝의 기반이 되는 수학을 배우는 것은 이 분야에서 성공하기 위해 매우 중요합니다.

온라인 강좌와 워크샵은 도움이 되지만 복잡한 수학적 개념을 가르치는 데 필요한 깊이가 부족한 경우가 많습니다.

기본을 넘어 적극적으로 교과서나 기본 소스 코드를 찾아 머신 러닝에 적용되는 수학을 이해하는 것이 중요합니다.