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Data Pipelines with TensorFlow Data Services(으)로 돌아가기

deeplearning.ai의 Data Pipelines with TensorFlow Data Services 학습자 리뷰 및 피드백

4.4
별점
449개의 평가
92개의 리뷰

강좌 소개

Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model. In this third course, you will: - Perform streamlined ETL tasks using TensorFlow Data Services - Load different datasets and custom feature vectors using TensorFlow Hub and TensorFlow Data Services APIs - Create and use pre-built pipelines for generating highly reproducible I/O pipelines for any dataset - Optimize data pipelines that become a bottleneck in the training process - Publish your own datasets to the TensorFlow Hub library and share standardized data with researchers and developers around the world This Specialization builds upon our TensorFlow in Practice Specialization. If you are new to TensorFlow, we recommend that you take the TensorFlow in Practice Specialization first. To develop a deeper, foundational understanding of how neural networks work, we recommend that you take the Deep Learning Specialization....

최상위 리뷰

PC

2020년 4월 16일

I understand why most of the students are furious about, but content wise, it one of those extremely helpful and important courses in Coursera. Really loved it!

GL

2020년 3월 2일

Laurence cares deeply about the students. Not only about what they learn, but that they actually enjoy and learn it. What a fantastic teacher.

필터링 기준:

Data Pipelines with TensorFlow Data Services의 92개 리뷰 중 51~75

교육 기관: Muhammad T

2021년 4월 28일

good course

교육 기관: Mr. J

2020년 5월 15일

Astounding

교육 기관: Insyiraah O A

2022년 4월 26일

very cool

교육 기관: mochammad g r

2021년 5월 9일

Thanks :)

교육 기관: Levina A

2021년 5월 24일

So cool

교육 기관: Mellania P S

2021년 5월 18일

AMAZING

교육 기관: Indah D S

2021년 5월 8일

great

교육 기관: Al F N P M

2021년 5월 29일

Nice

교육 기관: Ahmad H N

2021년 4월 29일

Good

교육 기관: 林韋銘

2020년 6월 16일

GJ

교육 기관: Cees R

2020년 3월 24일

I liked the topic and instruction of this course. I had bumped onto the notion of datasets earlier, was impatient as I needed to just resolve an issue, and skipped it. Next time I know what they are about and will be able - and happy - to use (including build) them.

Slight minus: presentation in the video often contained some bullets that I couldn't connect to the speech, that is, I had to choose: read or listen.

Bummer: the last week's exercise effectively required to copy-paste from a notebook that was scrolled through in the video. That is silly enough in itself. What is more, for certain errors in the created code in the notebook, the grader gave a standard notification that was not helpful in resolving the identifying what coding error had been made. As the discussion showed, a good number of people - me including - had been struggling with this to the level of feeling helpless to resolve it.

Still four stars for instructional value of the whole course, but I hope for the sake of future students that the above mentioned exercise will be replaced by a better one.

교육 기관: Matej M

2021년 3월 16일

This course was much better than the two before. Here were real exercises you had to do. It was not just about watching the videos. Topic itself was kind of boring. But the quality much better than others. Also on the discussion forum you were able to search for help.

교육 기관: Yopi P O

2020년 5월 17일

Debugging exercises due to errors in indentation sounded stupid in the first place. But the joy of finally getting a "yes" in the assignment auto-grader beats them all.

교육 기관: Vinay M

2020년 6월 5일

Was not that much engaging because the lectures were not linked properly and were lacking examples to support the content

교육 기관: Ruan V

2020년 8월 18일

Excellent content, but the design of Exercise 4 tainted the experience somewhat by the end.

교육 기관: Gregor S

2021년 2월 3일

Great explanation, however I believe in Week3 there are some broken links . . .heart.csv

교육 기관: Md S H C

2020년 8월 27일

Would be a 5-start if the last assignment was a bit well thought out.

교육 기관: Mario A C S

2020년 9월 16일

Last week exercise is problematic and not that educational

교육 기관: Chow K M

2020년 8월 10일

Coding exercises were generally straightforward.

교육 기관: Elyasaf E

2021년 4월 7일

More and better explanations are needed

교육 기관: Chuong L

2020년 4월 16일

week 4 wasn't very clear

교육 기관: Yong M L

2020년 9월 24일

This course is really interesting during the start, because there are a lot of hands-on for us to play around with the code. It shows the capability of TensorFlow as a Machine Learning framework which can also be used for data preprocessing before the model training.

However, the Assignments were very poorly designed in my opinion. I relied heavily on the discussion forums to pass the Assignments. It seems like a bug to me when you need to navigate through the Jupyter workspace to find the Week 5 notebook, then modify the codes from there and submit it to pass the Week 4 assignment.

교육 기관: İlkin H

2020년 7월 30일

Course content deserves 5 starts. Really nice and very useful tutorial. But assignments are not as good as the content. There are very less explanation , many mistakes ( specially at the last assignment, because of typo, you may submit several times) , no expected outcome to control before submitting (evaluation time of third assignment took around 30 minutes for me).

교육 기관: Vincent H

2020년 4월 14일

The content of weeks 1 to 3 is very useful and the videos are clear. However the content of week 4 goes way to fast, and the last exercice is way more difficult to do, and to validate. The number of questions in the forum on that matter illustrates that something could be improve. Though, thanks for the course!

교육 기관: Moustafa S

2020년 7월 3일

too much informations but for the most part it's made for the researchers, dealing with so many complicated methods and functions in tensorflow, which was helpful but the codes were too much and not described indepth, maybe you can improve the way you showcase the codes slowly and in different senarios.