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Structuring Machine Learning Projects(으)로 돌아가기

deeplearning.ai의 Structuring Machine Learning Projects 학습자 리뷰 및 피드백

35,191개의 평가
3,679개의 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


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.


Mar 08, 2018

Going beyond the technical details, this part of the course goes into the high level view on how to direct your efforts in a ML project. Really enjoyable and useful. Thanks for making this available!

필터링 기준:

Structuring Machine Learning Projects의 3,647개 리뷰 중 2926~2950

교육 기관: Max T

Jun 17, 2018

Was a good course with a lot of useful tips that I am sure I am able to use in my job as a data scientist. However, I would've liked if there were a few more hands-on examples (e.g. using jupyter) to really drives these concepts more home.

교육 기관: Nityesh A

Oct 10, 2017

The course could have been much shorter than it is because Andrew seems to be repeating his simple ideas a lot in the lectures. However, each simple advice seems important for practical purposes (I am willing to take Andrew's word for it).

교육 기관: Mikhail F

Oct 20, 2019

It might be not that trivial. But some hand-on experience with some code might be good here as well. As many practice as possible would be beneficial to the learners, coupled with great explanations from Andrew that are already in place.

교육 기관: Alon S

Sep 23, 2019

I think the quizzes should be considerably longer, to include more scenarios, and also have fewer questions that rest on technicalities (where some of the answers are almost correct except they misuse a term or give a wrong description).

교육 기관: Michael T

Oct 26, 2017

While the simulation is unique and very useful feature of this specialization. I believe examples with data would add to the leaning experience by allowing a student to actually run the scenarios and experience the qualitative changes.

교육 기관: Mark M

Nov 21, 2017

This course is at all an important part during the learning journey. The only reason why I not rate full 5 stars that the recommendation ramen little bit on high level and do not address typical frame conditions in real world projects.

교육 기관: Oliver M

Aug 16, 2017

Lots of practical stuff about training models. But you should try building a few models before doing the course. Otherwise, you may not fully appreciate how much time can be wasted unless you use Andrew's clear and logical approaches.

교육 기관: Wei Z

Oct 23, 2017

Lots of interesting and useful idea. Unfortunately the editing is poor and Professor Andrew Ng has gone a little bit repetitive in his talking in this course only. The two previous courses were great but this one is kind of dragging.

교육 기관: Saad T

Sep 07, 2017

I am a big fan of the jupyter notebook assignments. I can understand that it could be hard to build python assignments for this course, but not impossible I think (maybe around error analysis, impact of artificial data synthesis...)

교육 기관: S A

Jun 11, 2018

The content of the course lecture is great. The teaching is great. One problem is the quality of subtitles. The black background does not allow to see what is shown behind. It would be better if the background would be transparent.

교육 기관: Sarah W

Mar 21, 2018

Great material! Some of the videos went a bit long, and I think the point could have been made in much less time. However, overall this series has been great and I still got some very valuable info out of this course, so I'm happy.

교육 기관: Michael A

Dec 07, 2017

The course was very well structured and Andrews explanations was wonderful as usual. The only thing I was missing was more practical hands-on in the form of a programming exercise or two to really demonstrates the different ideas.

교육 기관: Ashwin m

Jul 01, 2019

this course provided very interesting insight into missing , incorrectly classified labels and also how existing models can influence the training of a new model which is on similar lines as the task the existing models performed

교육 기관: Silvério M P

Sep 06, 2018

Looking at practical examples is an enormous help and some concepts i learned here will undoubtedly be useful in the future, i just think there should be more of it. It's just really short both in duration as well as content

교육 기관: Vignesh S

May 28, 2019

It was really good to know how to structure and tune the nn so as to achieve a better model. But, I felt that it had too much theory in it that is hard to remember every time a model is to be designed. Overall, it was good.

교육 기관: Chandrashekar R

Sep 18, 2017

I rate the course high. Unfortunately many of questions (posed in the forum) have not been answered.

Her are some suggestions:

Have quiz after every lecture. That will firm up the concepts.

Give lesser help in assignments.

교육 기관: Gustavo S d S

Jan 04, 2018

Gives a sense about improving the performance of Deep Neural Networks, with error/bias/variance/data mismatch analysis. However, there is a lack of hands-on exercises, not having a programming assignment, only quizzes.

교육 기관: Michael F

Oct 20, 2018

Lots of useful tips and tricks in this course. I feel that the videos could have been a bit shorter, and it would have been nice to have some programming assignments. Overall the course was extremely useful, however.

교육 기관: Grant G

Dec 03, 2017

A pleasant diversion into practical considerations of project design. However the lack of programming assignments and the somewhat vague and fiddly quizzes make this a less satisfying course than it could have been.

교육 기관: Othman B

Jan 02, 2018

Very interesting, but too short. The aim of the course is to provide a good overview of the different situations occuring in a project, but there is more questions arising. Experience will come with training.

교육 기관: Antti R

Nov 03, 2019

nice to follow, but I would have liked it there would have been more variance. e.g. quizzes breaking the videos. I'm basically comparing this experiment with the other courses made by Andrew/

교육 기관: Dr S C

Oct 14, 2018

A useful few hours of videos. I found the questions quite useful, but overall feel this project would have been better off being spread across other weeks, as it doesnt work so well as a stand-alone course.

교육 기관: Sardhendu M

Oct 21, 2017

Very practical. programing assignment using the concepts would help to solidify the concepts. I would really appreciate programming assignments on Transfer learning since a lot of industries practices it.

교육 기관: Wiebe V

Aug 30, 2018

Clear course, it would have been helpful to add notebooks to the course to have a more realistic feeling of the problems. This would make it also more clear how the dev set influences the training phase.

교육 기관: Sandeep P

Jun 24, 2018

The course appraises the reader of the various tricks that are needed to design nice machine learning projects. One minor suggestion would be to have some programming assignments for this course as well!