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.
About this Course
학습자 경력 결과
공유 가능한 수료증
완료하는 데 약 22시간 필요
학습자 경력 결과
공유 가능한 수료증
완료하는 데 약 22시간 필요
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MATHEMATICS FOR MACHINE LEARNING: MULTIVARIATE CALCULUS의 최상위 리뷰
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.
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.
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.
Very clear and concise course material. The inputs given during the videos and the subsequent practice quiz almost force the student to carry out extra/research studies which is ideal when learning.
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 .
Excellent course!\n\nI studied multivariate calculus during engineering. I hardly understood the concepts at that time, this course helped me understand and visualize what is going behind formulas.
Superb quality. The way instructors teach is really innovative. The course is good in terms of the area it covers but lacks depth, but is a good starting point if you want to dwell more in detail.
I highly recommend this course.\n\nEvery Machine Learning student have to do it. Some concepts is so clearly explained that you will be able to perform better in following ML studies.
Just a great course for getting you ready to understand machine learning algorithms. The chapter on backpropagation is simply outstanding and the programming assignments are awesome!
As good as the first class in the Math for ML series. Instruction was interesting. Questions were not too confusing. Clearly a lot of time was spent producing this class. Thank you.
I wish, Linear Regression was taught with a little more clarity. Seemed like too many things were happening. Otherwise, a very good course. Really enjoyed the back-propagation week.
A wonderful course. I learnt a lot after struggling to finish it. Some foundations of calculus might be needed since the lecturer goes through differntiation in a tremendous speed.
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Imperial College London is a world top ten university with an international reputation for excellence in science, engineering, medicine and business. located in the heart of London. Imperial is a multidisciplinary space for education, research, translation and commercialisation, harnessing science and innovation to tackle global challenges.
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