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Вернуться к Mathematics for Machine Learning: Multivariate Calculus

Отзывы учащихся о курсе Mathematics for Machine Learning: Multivariate Calculus от партнера Имперский колледж Лондона

4.7
звезд
Оценки: 4,918
Рецензии: 881

О курсе

This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models. This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future....

Лучшие рецензии

DP
25 нояб. 2018 г.

Great course to develop some understanding and intuition about the basic concepts used in optimization. Last 2 weeks were a bit on a lower level of quality then the rest in my opinion but still great.

JT
12 нояб. 2018 г.

Excellent course. I completed this course with no prior knowledge of multivariate calculus and was successful nonetheless. It was challenging and extremely interesting, informative, and well designed.

Фильтр по:

651–675 из 884 отзывов о курсе Mathematics for Machine Learning: Multivariate Calculus

автор: Shanxue J

23 мая 2018 г.

Amazing

автор: Muhammad I A G

4 мар. 2021 г.

Greatt

автор: Liang Y

21 июня 2019 г.

Great!

автор: Shuvo D N

26 мая 2019 г.

Great!

автор: Nitish K S

18 июля 2018 г.

nice !

автор: Felix G S S

26 мар. 2021 г.

Great

автор: Sinatrio B W M

2 мар. 2021 г.

great

автор: Md. R Q S

21 авг. 2020 г.

great

автор: Kailun C

25 янв. 2020 г.

niubi

автор: 李由

23 авг. 2021 г.

good

автор: Dwi F D S M

23 мар. 2021 г.

good

автор: Ahmad H N

16 мар. 2021 г.

Good

автор: Habib B K

12 мар. 2021 г.

Nice

автор: Indah D S

27 февр. 2021 г.

cool

автор: RAGHUVEER S D

25 июля 2020 г.

good

автор: Nat

6 мар. 2020 г.

goot

автор: Zhao J

11 сент. 2019 г.

GOOD

автор: Harsh D

26 июня 2018 г.

good

автор: Roberto

25 мар. 2021 г.

thx

автор: Angel E E V

30 нояб. 2021 г.

:)

автор: Omar D

5 мая 2020 г.

gd

автор: Aidana P B

26 апр. 2021 г.

щ

автор: Naga V B G

7 авг. 2020 г.

.

автор: Rinat T

1 авг. 2018 г.

the part about neural networks needs improvement (some more examples of simple networks, the explanation of the emergence of the sigmoid function). exercises on partial derivatives need to be focused more on various aspects of partial differentiation rather than on taking partial derivatives of some complicated functions. I felt like there was too much of the latter which is not very efficient because the idea of partial differentiation is easy to master but not always its applications. just taking partial derivatives of some sophisticated functions (be it for the sake of Jacobian or Hessian calculation) turns into just doing lots of algebra the idea behind which has been long understood. so while some currently existing exercises on partial differentiation, Jacobian and Hessian should be retained, about 50 percent or so of them should be replaced with exercises which are not heavy on algebra but rather demonstrate different ways and/or applications in which partial differentiation is used. otherwise all good.

автор: yarusx

8 апр. 2020 г.

1) Totally British English with a bunch of very rare-used words and phrases globally. 2) The pace of the course is just not suitable for me. If you don't have strong math or engineer background you will need to search for the explanations somewhere else (khan academy - a great resource, etc.). Closer to the end of the course I stopped having a full understanding of what's going on and why. So I could calculate things, but I don't feel that I will able to that in 1-2 week because I didn't have a time and opportunity to strengthen gained skills. 3) Also I don't understand why instructors (especially David) don't visualize what they say like Sal or Grant are doing. They draw on the desk and on the plots and so on. Sometime it looks like you just listen to audio-book about the Math.

I will take Stanford ML course after this course and also review what I've learned here with Khan Academy resource.