返回到 Mathematics for Machine Learning: Multivariate Calculus

4.7

1,216 个评分

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180 个审阅

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....

创建者 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.

创建者 JT

•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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182 个审阅

创建者 ash goddard

•Mar 18, 2019

I am enjoying this course massively. I am on week 5 and the lecturer has been great so far. Some of the programming assignments are a bit easy as in some cases the blanks to fill in are rather self-explanatory.

The exercise questions progress in difficulty nicely and are sized well. References to tackle more questions to solidify the understanding could be good, however I recognise that the aim is to teach the intuition and then move on and apply it in Machine Learning examples, rather than being a mathematics course alone.

创建者 Miguel Vargas

•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.

创建者 danthedoubleD

•Mar 14, 2019

hopefully it is useful

创建者 J A Marin

•Mar 11, 2019

Excellent class! Understanding the math "under the hood" of the Python, Matlab, and R libraries is indeed the missing link holding back many data scientists from truly achieving competence and excellence. This course addresses such lacunae squarely by tackling a robust menu of relevant mathematical methods. Well done and kudos to Imperial College for taking the initiative.

创建者 Dawn Dunbar

•Mar 10, 2019

Really good introduction for things like regression and gradient descent. An extremely good refresher for calculus and extension from what is taught in school (in UK at least).

创建者 Jevon K Morris

•Mar 09, 2019

My current (2019.03.09) employer hates it. Therefore I love it.

创建者 Rishabh Joshi

•Mar 08, 2019

Not very challenging

创建者 Anbu Vasant

•Mar 04, 2019

Good revision for the calculus' application in ML

创建者 Arun Immaneni

•Mar 03, 2019

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

创建者 Sameen Notra

•Feb 22, 2019

It us good course and gave me basic understanding of multivariate calculus. It provide insight of gradient descent.