Mathematics for Machine Learning: Multivariate Calculus に戻る

4.7

1,791件の評価

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267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 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.

by Vignesh N M

•Sep 12, 2018

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

by PEI-YUAN C

•Sep 29, 2018

Along with the advanced and popular technique, this course gives me impressive insight over how machine learning works. But it would be much better if the concept in linear algebra combines more with this course.

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 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 Soon K G

•Aug 16, 2018

Good course to learn about the mathematics behind machine learning.

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 Long Q

•Oct 10, 2018

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by sreekar

•Oct 23, 2018

Great instruction and good materials.

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 Jose V R

•Jun 04, 2018

I think I've improve my understanding of mathematics for ML, mainly, I've understand much better the concepts involved

by Olena K

•May 21, 2018

Not enough detail in the explanations and little to no instructor participation in the forums.

by Arnaud J

•May 23, 2018

The course is still a bit young, some errors appear here and there sometimes, and some parts of it are a bit steep.

Otherwise, this is a good course, focused on derivatives.

by mrinal

•Jun 07, 2018

i think some of concepts touched the surface and it was difficult to get a deep understanding .Probably the course could have provided some external links for those topics where people could read .

by 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.

by Xiao F

•Apr 23, 2018

the basic concepts are explained clearly, but the step of the lecture became more fast than the course of linear algebra. More detail proof and application of theory is expected.

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 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 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 Philip A

•May 16, 2019

Excellent Instruction

by Harsh D

•Jun 03, 2019

Awesome course, just wish it had more info on hessian.

by Surinder D

•Jun 10, 2019

1.Week 5 should be taken in separate module dedicated to statistics.

2.The duration of course can be increased.

3. Week 3 and week 4 can be made more detailed

by Chika

•Jul 09, 2019

Feedback on assessment could be improved, and there could be more practice questions relevant to the final assessments

by 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