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Structuring Machine Learning Projects(으)로 돌아가기

deeplearning.ai의 Structuring Machine Learning Projects 학습자 리뷰 및 피드백

4.8
별점
46,681개의 평가
5,350개의 리뷰

강좌 소개

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....

최상위 리뷰

JB
2020년 7월 1일

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!).

MG
2020년 3월 30일

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.

필터링 기준:

Structuring Machine Learning Projects의 5,295개 리뷰 중 4251~4275

교육 기관: P M K

2017년 11월 30일

Hi, This course though very useful had become a bit monotonous and at times a bit difficult to understand. There could have been better presentation giving more examples. The Quiz had really tough questions , in some cases the language is not clear. I request the course mentors to look into the same. Nevertheless, it has definitely been very useful as it highlights the practical problems faced and ways to resolve them

교육 기관: Shuai X

2017년 12월 15일

This Course offers simple, useful and general tips for starting a typical deep learning project. The most valuable part is on how to split datasets and how to identify possible data distribution mismatch. The tips and case studies do not always work in real application. But that is perhaps because the course is intending to be simple. This course does not require any math backgrounds and can be completed in 4 hours.

교육 기관: Harry ( D

2018년 8월 12일

Although I see other learners saying that this is the worse of all the Deep Learning specialization courses because there are no programming assignments, I believe it was a very useful course full of practical knowledge for properly structuring ML projects. I agree, however, that video quality is worse that the other courses and there are some editing issues (some video segments repeat, blank sections, etc.)

교육 기관: David R R

2017년 11월 16일

This course is hard to complete because the lessons are very large and difficult to understand. However, I recommend this course for anyone than want to apply deep learning in real enterprise world.

Este curso es dificil de completar ya que las lecciones son muy largas y costosas de entender. Sin embargo, recomiendo el curso para todo aquel que quiera aplicar deep learning en el mundo empresarial.

교육 기관: Abdulsalam A

2021년 3월 3일

I like the explanation and the scenario, but I miss the implementation.

If you add some real implementation about transfer learning using a framework like TF (MobileNet) or PyTorch, that will be great.

I hope you can improve the transcript (Arabic)

I found many times it looks like you are using Google translation. Please give in each class the operantly to correct the transcript.

Thanks a lot

교육 기관: Jason T

2018년 5월 27일

I liked this course a lot, since it introduces transfer learning and multi-task learning and so moves you toward more powerful and realistic AI applications than the previous courses in the specialization. However, I missed the programming assignments that aided understanding so much in the previous courses. The quiz by itself was not as effective at illustrating the key concepts.

교육 기관: Matt E

2018년 4월 8일

I wouldn't really consider this a "course," but the stuff he taught was great. However, Andrew could go much deeper into these topics. Some real data examples that he has come across would be even more helpful. Seeing how he codes his approaches in python would also be a very useful (and quick) batch of lectures. If he needed to extend it another week that would be understandable.

교육 기관: Stoyan S

2017년 10월 1일

Excellent course just like the previous two. Short programming exercises would have been nice to have. Some of the answers in the quiz were too similar and this might be quite confusing for non-native English speakers and therefore can reflect more knowledge in English language rather than knowledge in related machine learning topics. I am looking forward for the next 2 courses.

교육 기관: Amir N

2021년 5월 30일

This could be a more useful course if it came after convolutional neural networks and sequence models courses. In that case, the learner could practice some of the strategies on the models that he had previously developed. Right now, most of the strategies will be forgotten by the time that the learner reaches to a point that can confidently develop large deep learning models.

교육 기관: Mikko H

2017년 9월 24일

Great material that's clearly based on valuable practical experience. I and found the "machine learning flight simulator" quizzes to be an educational format. However, the editing of the quiz questions (grammar, matching question types with wording in the question etc) was not flawless in September 2017. This course would benefit from another review pass from this perspective.

교육 기관: Kévin S

2018년 7월 31일

This course is clear, and show how a machien learning project should be driven. But there is two problem : First it is entierly theorical : no pratical exercices (so it is only 4 stars) ; second it did not speak of a big problem : How make your boss understand that if you use the "test" set too mush, it become another "developpement" set -> without using sciences words...

교육 기관: 张子威

2018년 3월 7일

Overall, a great course for designing deep learning projects, which gives a lot of insights and tips that typically not taught at university classes. However, there does exist some minor problems related to video editing and quiz problems. I suggest the lecturer or staff of the course put more efforts in dealing with them (and maybe attend more to the discussion forums).

교육 기관: Yen-Chung T

2017년 9월 25일

This course gives an overview on how to address common problems faced during machine learning projects. Although these experiences can prove valuable, for average people that may not be actively involved in machine learning, the information may sound like "common sense". The course may benefit with a more abundant set of real-world practice scenarios for analysis.

교육 기관: Saurabh D

2020년 4월 2일

This course was totally different from the previous two courses. It was focused more on the theoretical aspects of how to approach and build ML projects, difficulties that ML engineers can face and how to avoid them. The content of this course could have been more interesting if more real world problems were included and if there were some programming exercises.

교육 기관: KUMAR M

2020년 2월 11일

A very nice course to teach how to start a data science project, how to evaluate it, how to select path ahead improving the model, what all to be taken in consideration before training or while training or after training.

Some more case studies could be added since the course is smaller in length and case studies are helping a lot in making understanding clear.

교육 기관: Carlson O

2017년 10월 20일

Again, great course. Congratulations. This time, i've missed some programming assignments, although the case studies was very instructive of the practice, some programming experiments with transfer learning will be great. Nevertheless, the course has extremely valuable knowledge to those, like myself, that want to practice in real problems and corporate world.

교육 기관: Carlos d l H P

2020년 1월 27일

Actually adds some insights I hadn't learned (or at least I was guessing but it's always nice to have a double check) after 4 years as a data scientist.

Also, some of those insights are very specific to neural networks projects, so doesn't matter how many years have you been working if you've never made deep learning projects this will help you nevertheless.

교육 기관: Ching-Chih L

2018년 5월 17일

This two-week course gave very important concepts. However, there's no programming assignments and lectures are lengthy. It felt a little "boring" for a hand-on guy like me.

That being said, one should not skip these important lessons if he/she wants to take charge of ML projects one day instead being a programmer who only takes orders from others for life.

교육 기관: Arthur O

2018년 2월 28일

This course gave a lot of practical advice and is excellent material to combine with the more programming-focussed lectures of the deeplearning.ai series.

Small points of criticism are that I thought some videos could have been a bit shorter/less repetitive and there were quite a few language mistakes in the quizzes (missing words and grammatical errors)

교육 기관: José D

2019년 9월 26일

Course 3 of the Deep Learning Specialization. There is no coding in this one but longer quizzes which require you to fully understand the concepts and recommendations given in the course. It's all about ML project strategy and how to manage you results and errors. Quite interesting and important for the general understanding of a Deep Learning project.

교육 기관: Chris L

2017년 9월 6일

I liked this course overall and found it to be very informative. I, personally, was a little thrown by the eclectic nature of the course's materials. Sometimes it seemed as if the material covered in each week was only loosely related, or was thematically similar for part of the week, but then the last few videos were on something else entirely.

교육 기관: P S R

2017년 11월 15일

It is too much of theory, with significant repetition from machine learning course and within Deep Learning course 1 and course 2. It would have been lot of help if we had programming exercise on transfer learning, data synthesis and multi task learning to get a hang on practical experience, similar to first 2 courses of Deep Learning!

교육 기관: moonseok s

2018년 6월 10일

thank you for to teach how to research and it will be of great help to real researchers.All theories have been a pity not try because I did not get a lot of the actual study. I think it will be a great help for future research opportunities.It is very difficult to study because it is not practical. but in future it will very helpful.

교육 기관: Jesús A G Z

2020년 7월 28일

Although purely conceptual, the course really gives good advice on how to come up with a work flow to react to errors due to data, and the metrics that can be used as reference. I just wish there was an assignment where you could see a NN working with mismatched data and how it reacts to some of the improvements that were mentioned.

교육 기관: Anshul M

2017년 10월 31일

Course contents are great as it talks about how to improve performance by giving real world example. This is one of the most crucial pieces in any model building task, but still is less focused in traditional courses. Andrew Ng's team has dedicated a full course on this aspect, which I believe will do the learners a huge benefit!