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deeplearning.ai による Structuring Machine Learning Projects の受講者のレビューおよびフィードバック

4.8
47,736件の評価
5,477件のレビュー

コースについて

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader. By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning. This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

人気のレビュー

MG
2020年3月30日

It is very nice to have a very experienced deep learning practitioner showing you the "magic" of making DNN works. That is usually passed from Professor to graduate student, but is available here now.

JB
2020年7月1日

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!).

フィルター:

Structuring Machine Learning Projects: 5401 - 5425 / 5,441 レビュー

by Peter E

2018年5月2日

Too theoretical. It would be good to have some practical (programming) assignments here as well.

by Mohamed E

2017年11月22日

Not much to learn in this course, basic recommendations can be condensed in one or two lectures

by Jordi T A

2017年8月28日

A lot of the content seemed redundant both within the lectures and with the previous courses

by Clement K

2020年5月11日

Interesting but redundant. It's not worth an entire course, even if it's only two weeks

by Péter D

2017年10月6日

long videos saying actually very little ... disappointment

by Andrey L

2017年10月29日

Quite boring and not so interactive like the first course

by harsh s

2020年9月22日

good but more theoretical course rather than pratical

by Kaarthik S

2020年5月25日

this is the boring course in the specialization

by Thomas A

2019年10月2日

Can be better, but there's way too much fluff

by Till R

2019年3月2日

Some things are best learned from experience.

by Subhadeep R

2018年9月25日

Frankly I didn't find this to be very useful.

by Hernan F D

2019年12月17日

There is no a lot of content in this course

by Aloys N

2019年9月20日

Missing a bit of practical Python exercises

by Ofer G

2019年7月9日

Pretty basic and not enough practical

by 2k19ec173 s

2021年4月4日

please work on the audio quality

by Agniteja M

2019年10月2日

Useful only for beginners

by Chaobin Y

2017年10月12日

Too little materials.

by Vinayagamurthy.M

2020年1月5日

Very theoritic

by Gerrit V

2019年8月19日

Much too slow

by Zeyi W

2018年4月8日

Too short

by Christof H

2017年9月18日

no praxis

by 태윤 김

2018年7月9日

no funny

by NATHAN W

2021年5月3日

extremely overgeneralized with no information on how to apply any of these concepts to an industry application. No manufacturing facility birdwatches as a source of income.

by Sergey A

2017年12月1日

Extremely boring course without any practice. All the topics explained could be summarized on a dozen of pages which are obviously much easier and faster to read.

by Gianluca M

2018年3月16日

What is this doing here? Just useless. No science, only insights that would be valuable were they not already given in the other courses of the specialization.