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Structuring Machine Learning Projects(으)로 돌아가기

deeplearning.ai의 Structuring Machine Learning Projects 학습자 리뷰 및 피드백

4.8
별점
42,141개의 평가
4,730개의 리뷰

강좌 소개

You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience. After 2 weeks, you will: - Understand how to diagnose errors in a machine learning system, and - Be able to prioritize the most promising directions for reducing error - Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance - Know how to apply end-to-end learning, transfer learning, and multi-task learning I've seen teams waste months or years through not understanding the principles taught in this course. I hope this two week course will save you months of time. This is a standalone course, and you can take this so long as you have basic machine learning knowledge. This is the third course in the Deep Learning Specialization....

최상위 리뷰

JB

Jul 02, 2020

While the information from this course was awesome I would've liked some hand on projects to get the information running. Nonetheless, the two simulation task were the best (more would've been neat!).

AM

Nov 23, 2017

I learned so many things in this module. I learned that how to do error analysys and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.

필터링 기준:

Structuring Machine Learning Projects의 4,679개 리뷰 중 4201~4225

교육 기관: Peter K

Mar 23, 2019

강의 후반부 (2주차) 에 강의 속도가 인위적으로 조정된거 같습니다. 속도가 빨라 이질감이 느껴졌습니다.

교육 기관: Akshat A

Feb 23, 2019

Curated content, quite exclusive indeed. Respect to Dr. Ng

교육 기관: Joseph C

Apr 10, 2018

Needs programming exercises to help firm up the new ideas.

교육 기관: QUINTANA-AMATE, S

Mar 20, 2018

Completely new of what it is out there. Well done Andrew!!

교육 기관: Alejandro R V

Jan 08, 2018

Not as interesting as the others, I personally prefer math

교육 기관: Gopala V

Oct 24, 2017

Gave some ideas on mismatched data and how to address them

교육 기관: Akshita J

Apr 23, 2020

An assignment could have been included to let practically

교육 기관: Roberto J

Oct 19, 2017

A bit dry, would love to see some more concrete examples.

교육 기관: Vinicius B F

Oct 23, 2017

Content was fantastic, but the videos were badly edited.

교육 기관: Suresh P I

Sep 10, 2017

Can be potentially folded into other courses if possible

교육 기관: heykel

Jan 27, 2020

very helpful to build an intuition for DL strategies...

교육 기관: Rafael G M

Dec 07, 2019

Providing further references would benefit this section

교육 기관: WEIJIAN K

Nov 15, 2017

You can know well a lot of strategy in machine learning

교육 기관: B S K

Jul 14, 2020

Good teaching of practical approaches and nice quizzes

교육 기관: 王毅

Dec 24, 2019

the content is good, but the videos are not well made.

교육 기관: Shuochen Z

Feb 18, 2019

内容架构很好,讲得也很实用,但觉得课时有些短,许多重要且有趣的问题都未能得到展开详述。期待后续的扩充课程~~

교육 기관: Gundreddy L M

Sep 11, 2018

excerice should be given for this one proper user case

교육 기관: Alexey S

Oct 23, 2017

Good class, but 2 previous are much better and useful.

교육 기관: Lei C

Sep 25, 2017

the answer of the assignment is a little bit arguable.

교육 기관: Kumari P

May 28, 2020

machine learning project are highly iterative as you.

교육 기관: diego s

Feb 18, 2020

I miss notebooks for practice, besides questionnaires

교육 기관: Xinghua J

Sep 06, 2019

If there is some coding practice, it would be better

교육 기관: Pranjal V

Jul 11, 2020

Very well explained but needs more reading material.

교육 기관: Hee S K

Apr 18, 2018

It is an unique lecture providing empirical advises.

교육 기관: Pablo L

Oct 30, 2017

Great set of guidelines. Mostly theoretical, though.