Вернуться к Mathematics for Machine Learning: Multivariate Calculus

4.7

(оценок: 1,442)

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....

автор: 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.

автор: 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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Рецензии: 214

автор: Lee jung sok

•May 23, 2019

Too fast to understand what instructors says.. but lecture contents are good

автор: Phạm Ngọc Minh Huy

•May 23, 2019

This is one of three course in Mathematics for ML, it'll give you intuition for understand the true meaning of ML/DL/AI , it's all about math

автор: Alberto Matuozzo

•May 20, 2019

Professors have done a great job in explaining clearly a complex subject

автор: Vibhutesh Kumar Singh

•May 18, 2019

I think neural networks was unnecessary. It was very concise to understood by anybody without prior knowledge about it,

автор: Philip Abraham

•May 16, 2019

Excellent Instruction

автор: JUNXIANG ZHU

•May 16, 2019

As a physics graduate, this course serves a fresh up in calculus and optimisation, which is essential for studying machine learning.

автор: Navaneeth Malingan

•May 15, 2019

Excellent course. Must do course for Machine Learning Developer.

автор: Jafed Encinas

•May 14, 2019

Able to concentrate and stay focused for periods of several hours, even when tasks are relatively mundane, and doesn't make mistakes. He has a high boredom threshold. Always assured and confident in demeanour and presentation of ideas without being aggressively over-confident. No absences without valid reason in 6 months. Reaches a decision rapidly after taking account of all likely outcomes and estimating the route most likely to bring success. The decisions almost always turn out to be good ones.

This Course always completes any assignment on time and to a high standard. This Course has outstanding artistic or craft skills, bringing creativity and originality to the task. Aiming for a top job in the organization. He sets very high standards, aware that this will bring attention and promotion. This Course pays great attention to detail. He always presented work properly checked and completely free of error.

автор: Santosh Bangera

•May 14, 2019

A well structured course packing a lot of learning content and taught by good lecturers.

автор: Matthias Schniewind

•May 13, 2019

The first four weeks are excellently prepared and the programming assignments are almost too easy at some points. The last two weeks and a part on backpropagation in the first four weeks give a nice intro on how to apply the learned methods. In the last two weeks there were some minor flaws in some slides and it is less easy to follow but it is still very well presented.

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