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Structuring Machine Learning Projects(으)로 돌아가기

deeplearning.ai의 Structuring Machine Learning Projects 학습자 리뷰 및 피드백

4.8
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

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.

DC

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 3,648개 리뷰 중 3576~3600

교육 기관: Leticia L R

Aug 12, 2018

Bit boring.

교육 기관: Wouter M

Jun 13, 2018

A bit short

교육 기관: Zhen T

Dec 20, 2019

Too simple

교육 기관: Gonzalo A M

Jan 17, 2018

Too short.

교육 기관: My I

Mar 16, 2019

too easy

교육 기관: Артеменко Е В

Sep 03, 2017

Too easy

교육 기관: Sajal J

Oct 29, 2019

okay

교육 기관: KimSangsoo

Sep 18, 2018

괜찮음

교육 기관: Benedict B

Jul 27, 2018

ich

교육 기관: Shawn P

Jun 08, 2018

k

교육 기관: Daniel S

Mar 20, 2018

Definitely not worth paying for (and I literally completed this in one afternoon). Thankfully I did not pay, so it was not that bad value in fairness.

In honesty the lack of value from this course actually says a lot about Andrew Ng's original Machine Learning course, which was consistently excellent. Actually coding in Octave for that class cemented a lot of concepts as well, which this course does not.

The title of the course suggests this is pitched towards more advanced students who already know about Machine Learning but maybe not so much about best practices. This feels far too basic for that demographic. The practices are sensible though and useful, if maybe overly focussed on massive datasets as opposed to the ones that Google *doesn't* deal with on a daily basis. Things like SMOTE could have been mentioned as well, for example.

TL;DR: This feels like a missed opportunity. My advice is don't take it if you've done Andrew Ng's ML course. Google things after that and wait for a decent course that's pitched towards intermediate students.

교육 기관: Marina R

Oct 18, 2017

I found the course rather confusing than helpful. One of the key issues with video-only courses is lack of interaction of the user with the material. In previous Andrew's ML courses, this issue was cunningly tackled with "wake-up" multiple choice mini-quizzes. Such techniques would help the course a lot.

The questions in the exam were poorly phrased and full of typos; some had numerical issues (percentage of errors in the dev set did not sum up). Some of the answers seemed to contradict with the material as I remembered it from the course: f.e., the question on whether to get more foggy images to improve the model performance should have been answered with "augmentation is fine as long as it looks fine to the human eye". This contradicts to Andrew's remarks in the course video "Addressing data mismatch" video -> Artificial data synthesis. Are you sure we would not introduce a bias by adding artificial fog to frontal camera images?

교육 기관: Gilad F

Nov 17, 2019

Notwithstanding the great video lectures this course's assignments were poorly composed:

Firstly, there are no programming assignments! I understand the material here is mostly conceptual, however subjects such as 'Transfer learning' and 'Multi - task learning' should be given as a programming assignments. In 'Transfer learning' you need to modify an existing model, which I think is a good tool for a student. Hopefully we will use it in future lessons. Lastly some of the questions in both 'quizzes' have many complaints in the forum and the same complaints reappear yearly, therefor it's a bit annoying no measures are taken to modify the questions so they will be clearer.

교육 기관: Ashvin L

Aug 25, 2018

The 3rd course is more art than science. There is a lot of breadth, but we cover each topic in passing. Therefore, from a student perspective, I find that the concepts are not cemented and it is entirely possible that I forget them once I move on to the next course.

The second issue I find with the course is that there are no programming assignments. Programming assignments. Programming assignments are key to understanding such complex topics and getting the idea cemented. It would have been much better, if we could cover each topic such as data-mismatch, comparison to human level performance, etc via assignments.

교육 기관: Sreemanananth S

Oct 01, 2018

Very verbose with hand-wayy examples. The 18 minute lecture was the hardest Ive tried to not fall asleep. The second quiz has extremely badly written questions with multiple choice answers. Very ambiguously worded QnA. Don't mistake this review for the whole DL specialization though. Andrew's DL specialization course is brilliantly structured and an excellent primer for folks such as myself just getting into DL. It is only this section on structuring ML projects which is a little bit of a drab.

교육 기관: Younes A

Dec 07, 2017

The material is great, but the production quality is so poor that I had to give 4 stars only. Videos have blank and repeating segments, and more quizes have mistakes that make getting a 100% because you know the material impossible (you have to tolerate some wrong answers to do it). This means you can't rely on quizes at all, because maybe the ones you got right were actually wrong :). The ones I got wrong were also called out by other people on the forums, so I guess maybe I am right.

교육 기관: Guilherme Z

Sep 04, 2019

The most exciting part of the course as others in the series is the interviews that Andrew does with deep learning researchers. I thought I would learn more about how to structure actual machine learning projects from a software perspective and how I would incorporate them to real products. I felt the videos for this course were too long and cover somehow basic common sense.

교육 기관: ni_tempe

Sep 14, 2017

the course doesnt have any programming assignments. I feel that these two weeks should have been added/combined with first 2 courses. The knowledge that is provided is useful, but it is mainly useful once you are an expert at building neural networks and models. I feel that this course should have been the last course in the series instead of the 3rd course

교육 기관: Markus B

Sep 06, 2017

Just a few videos without any programming excercise or a bunch of rather broad statements that are not really tried out in programming examples are not really worth the money and more importantly the time. The first two courses are good, this is definitely a drop in terms of quality. This one needs more meat on the bone.

교육 기관: Jim M

Aug 26, 2018

This had the potential to be a very good course, but fell far short, in my assessment. It probably should have been rolled into the previous course as a couple of additional lectures.

Either that, or it should be expanded greatly, with more practical exercises to solidify the concepts taught.

교육 기관: Ted S

Dec 19, 2017

Doesn't look like it was checked for quality control (e.g. Videos with bad takes), Ng rambles sometimes so that it seems as if he is filling time, there are no knowledge checks. This course wasn't ready. Case study flight simulators are good, but poorly introduced.

교육 기관: Tiffanie B

Oct 18, 2019

The teacher seemed to not have a clear idea of what all he needed to say in the video and verbally flailed somewhat, and many times seemed to be adding things purely for the sake of padding video length. Don't waste my time. The content was mildly useful.

교육 기관: HAMM,CHRISTOPHER A

May 07, 2018

I need a lot more practice than is offered here. I would also strongly prefer if the instruction followed some of the best practice laid out in books such as "How Learning Works" because I have difficulty following the instructor's line of reasoning.

교육 기관: Leonardo M R

May 27, 2018

Answers in the multiple choice seems incomplete for me, I don't necessary agree with the answers presented unless more detail on some context (that I don't think we should assume) is present for the questions.

교육 기관: Пильгуй В Л

Dec 03, 2019

This course is good, but here is so few practice. This is hard to understand without practice. Looks like I didn't understand many problems in this course. Need more explanation, more samples, more practice.