Mathematics for Machine Learning: Multivariate Calculus に戻る

4.7

1,923件の評価

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289件のレビュー

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....

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.

Nov 13, 2018

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.

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by Angelo O

•Dec 05, 2018

Nice refresher! Excellent instructors! Not recommended as a first Multivariate Calculus course though. I would go for MIT OpenCourseware first.

by Mark R

•Jan 03, 2019

Good grounding in fundermental material needed for ML

by Satpal S R

•Jan 30, 2019

This was a great course for learning multivariate calculus required for Machine Learning. I am thankful to the creators of this awesome course.

by Patrick F

•Feb 01, 2019

Really good course, would recommend! 4 Stars, because there is no written transcript with the Formula and examples in the videos available.

by mnavidad

•Nov 25, 2018

This course was super hard but worth it...

by Adam N

•Nov 12, 2018

Very nice and concise, definitely review normal calculus and look up materials to get the most out of this course.

by Paulo G

•Nov 29, 2018

great content, but the grader software needs some improvement

by Wenyuan Z

•Jan 11, 2019

Well the course is generally good, the only problem is that David sometimes may just skip the process and lack more explanation when performing the calculation, it's easy to lose track of what he is calculating if not reviewing the video over and over again, but anyway, the whole class is worth recommendation, thank you for your teaching, professors

by Dmytro B

•Feb 11, 2019

Very helpful to review and get introduced to mathematical concepts behind machine learning. There is a fair bit of practical exercises as well. The only thing I am less happy about this cousre was a lack of additional suporting materials and references to other resources to help gain more knowledge on the subject.

by Hariharasudhan A S

•Feb 12, 2019

Really good for fundamentals, the assignments were too easy though

by Prashant D

•Feb 17, 2019

Good course. The lecturer uses a number of illustrations and has a nice easy style to explain the key ideas. Overall enjoyable

by Arun I

•Mar 03, 2019

Good course to understand the basic mathematics terms and a refresher for high school math with some technical terms.

by danthedoubleD

•Mar 14, 2019

hopefully it is useful

by Miguel V

•Mar 16, 2019

I think Samuel Cooper is an amazing instructor. However, the last two weeks taught by David Dye were very difficult to follow. I think David should improve his explanations because I did not enjoy too much his course on linear algebra, and this course was great until he started with the last two weeks.

by Hemant D K

•Dec 17, 2018

Its good.

by 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.

by Matt P

•Jul 19, 2018

Great class - very informative and eye opening - even with quite a bit of linear algebra background. Really liked the eigenvector and eigenvalue section - great descriptions. I wish the neural network discussion went on a bit further. I found some of the programming assignments' instructions a bit vague and confusing - what should have taken a few minutes ends up taking a half hour.

by Daniel P

•Aug 22, 2018

Interestin to refresh notions you already learned. Probably a bit difficult if you're totally new to multivariate calculus

by Girisha D D S

•Aug 26, 2018

I thoroughly enjoyed this course. The materials were good and the course content was good enough to pass all the assignments and quizzes. This is way better than the linear algebra course in this specialization.

by Long Q

•Oct 10, 2018

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by José D

•Oct 18, 2018

Very instructive, good refresher of multivariate calculus in the context of machine learning

by Aneev D

•Oct 19, 2018

This course is great in the sheer efficiency with which it goes through the content required to prepare you for machine learning. It builds an intuition for what's going on, which is amazing. Some parts are confusing, and I recommend looking at Khan Academy for the lectures on Jacobians and steepest ascent, and 3Blue1Brown for feedforward neural networks.

by Vignesh N M

•Sep 12, 2018

It was a good course compared to other two courses of this specialization.

by SUJITH V

•Sep 16, 2018

Very good course to start of with mutivariable calculus basics. Helps to refresh your memory if already familiar with concepts, additionally helps in getting fresher perspective because of geometrical intuition presented very well.

by George K

•Sep 21, 2018

Lack of support from the staff. Some parts/lectures are not clearly explained (for example, constrained optimization) and some quiz questions are not directly related to the course content. Otherwise, it's a very good course.