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返回到 Mathematics for Machine Learning: Multivariate Calculus

学生对 伦敦帝国学院 提供的 Mathematics for Machine Learning: Multivariate Calculus 的评价和反馈

4.7
1,504 个评分
221 审阅

课程概述

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....

热门审阅

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.

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.

筛选依据:

1 - Mathematics for Machine Learning: Multivariate Calculus 的 25 个评论(共 227 个)

By Tanuj J

Jan 19, 2019

Topics need to be covered more in depth. Too much information packed into this course. Instructor's explanations are also not clear most of the time. It will be hard to follow this course if you don't have some background with calculus.

By Marc P

Apr 28, 2019

The course is led by two instructor and my ratings is an average of the two performances. The videos in week 1 to 4 are absolutely outstanding and a pleasure to follow. The ones in week 5 and 6 are ok but not great. The use of quizzes and coding assignments throughout the course is very engaging and of great use for retention and application of the learned subjects.

By João C L S

Apr 17, 2019

I liked the course specially because I finally understood Backpropagation, an old frustration from Andrew Ng's Machine Learning course. It covers the main topics for Mathematics for Machine Learning as promised. Two weak points: (1) the Newton-Raphson convergence problems, superficially covered in the lectures, but has a challenging test, no forum support, no other source indicated for helping us. (2) The forum is abandoned. I've set two problems, one of them about an error in a lecture and the second about the problem with Newton-Raphson lecture. No responses from the lecturers or mentors.

By Yan

Mar 31, 2019

Some errors confused many students. And they are remained unfixed.

By Andrii S

Jan 20, 2019

Excellent.

By Fabian B

Jun 16, 2019

Challenging, but very exciting. Learned a lot interesting fundamentals. Thank you.

By Gautham T

Jun 16, 2019

fantastic course

By Shahan C

Jun 15, 2019

Helped me understand the underlying match concepts in machine learning and did help me professionally.

By phu c d

Jun 11, 2019

The course is very useful with very interesting quizzes and assignments

By BALAJI R

Jun 10, 2019

That's some excellent course to take for! Awesome explanations for the concepts and I strongly recommend khan academy for further explanations.

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 Rishabh A

Jun 09, 2019

Loved the course. Backpropogation section needs more elaborate explanation, where are we doing dot products, where are we doing matrix multiplications, things go confusing.

By Harsh D

Jun 03, 2019

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

By Мусаллямов Д Н

May 31, 2019

Awesome!

By Leigh F

May 29, 2019

Concepts not clearly explained.

By Kris S

May 28, 2019

Very well taught, great flow of lectures, awesome teachers!

By pymo2504@gmail.com

May 27, 2019

El curso explica de forma sencilla, los conceptos de calculo necesarios para la comprensión del aprendizaje de máquina. Realmente muy útil. Además, incluye ejercicios prácticos en python

By Shuvo D N

May 26, 2019

Great!

By Lee j s

May 23, 2019

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

By Phạm N M H

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

By Alberto M

May 20, 2019

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

By Vibhutesh K S

May 18, 2019

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

By Philip A

May 16, 2019

Excellent Instruction

By JUNXIANG Z

May 16, 2019

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

By Navaneeth M

May 15, 2019

Excellent course. Must do course for Machine Learning Developer.