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返回到 Mathematics for Machine Learning: Multivariate Calculus

学生对 伦敦帝国学院 提供的 Mathematics for Machine Learning: Multivariate Calculus 的评价和反馈

4.7
2,607 个评分
413 条评论

课程概述

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....

热门审阅

SS

Aug 04, 2019

Very Well Explained. Good content and great explanation of content. Complex topics are also covered in very easy way. Very Helpful for learning much more complex topics for Machine Learning in future.

DP

Nov 26, 2018

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.

筛选依据:

301 - Mathematics for Machine Learning: Multivariate Calculus 的 325 个评论(共 415 个)

创建者 NARALA P R

Apr 02, 2019

very good

创建者 Yash V P

Mar 25, 2019

very cool

创建者 Nidal M G

Nov 11, 2018

very good

创建者 Edward K

Sep 04, 2018

very nice

创建者 Bielushkin M

Jun 08, 2018

retretret

创建者 Kuo P

Mar 15, 2018

excellent

创建者 Rodrigo F

Sep 18, 2019

Amazing!

创建者 Мусаллямов Д Н

May 31, 2019

Awesome!

创建者 James A

Jan 14, 2019

Amazing!

创建者 AMIT K A

Jul 27, 2018

V

E

R

Y

G

O

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D

创建者 Wong Y W M

Feb 21, 2020

Thanks.

创建者 Bálint - H F

Mar 20, 2019

Great !

创建者 Shanxue J

May 23, 2018

Amazing

创建者 Fish

Jun 21, 2019

Great!

创建者 Shuvo D N

May 26, 2019

Great!

创建者 Nitish K S

Jul 18, 2018

nice !

创建者 Kailun C

Jan 25, 2020

niubi

创建者 Nathan L

Mar 06, 2020

goot

创建者 Zhao J

Sep 11, 2019

GOOD

创建者 HARSH K D

Jun 26, 2018

good

创建者 Rinat T

Aug 01, 2018

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.

创建者 Ronny A

Jun 27, 2018

Course is pretty good. I like how well thought out the assignments are and the use of visualizations, even in the assignments, to enrich intuitive understanding. There were a couple of instances where the content wasn't clear and I referenced Khan Academy to clarify things for myself. The reason I give this course a 4-start rather than a 5-star is that it seems the teachers or else TAs were not responsive. Specifically, myself and another person had posted in the discussion forum how it seemed one of the slides had a typo in the Jacobian contour plot. There was no official response to this.

创建者 Fang Z

Jul 11, 2019

I really love Samuel's teaching style. He strived to make people understood by showing a lot of graph and I can easily follow him step by step. However, David's teaching I couldn't follow up his mind much maybe because less explanations given during the lecture.

In addition, I found some quiz have huge amount of calculated amount which I really spent a lot time to verify the answer.

Finally, I hope more detailed explanations could be given if I made mistakes in some quiz so I could boost what I've learned so far.

Thanks,

Fang

创建者 Hermes J D R P

Feb 28, 2020

The first 4 weeks of the course were amazing: great content, clear explanations and fair and interactive assessment activities. However, the last 2 weeks weren't as good as the previous ones. That's why I don't give this course 5 stars. By and large, the first two courses of this specialization are the best resources available on the internet to learn the foundations of mathematics for Machine Learning. I recommend that instead of doing the last course, you had better try to read the related book wrote by Deisenroth.

创建者 Saras A

Jan 29, 2020

Good course. I wish it had more sections as in a total of 12 sections or weeks and more steps to gain a more thorough graphical understanding (and perhaps even a more mathematical/algebraic understanding however overall that's much easier for me on that front...).

From a Data Science or Machine Learning perspective Week 6 (linear regression and non linear regression with chi-squared methods etc) were the most interesting.