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Learner Reviews & Feedback for Data Pipelines with TensorFlow Data Services by DeepLearning.AI

4.4
stars
512 ratings

About the Course

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....

Top reviews

PC

Apr 16, 2020

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

Mar 2, 2020

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

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76 - 100 of 105 Reviews for Data Pipelines with TensorFlow Data Services

By Ruan V

Aug 18, 2020

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

By Grega S

Feb 3, 2021

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

By Md S H C

Aug 27, 2020

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

By Mario A C S

Sep 16, 2020

Last week exercise is problematic and not that educational

By Chow K M

Aug 10, 2020

Coding exercises were generally straightforward.

By Elyasaf E

Apr 7, 2021

More and better explanations are needed

By Chuong L

Apr 16, 2020

week 4 wasn't very clear

By Yong M L

Sep 24, 2020

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.

By İlkin H

Jul 30, 2020

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).

By Vincent H

Apr 14, 2020

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!

By Moustafa S

Jul 3, 2020

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.

By X. B C

May 18, 2024

Awesome content, but most code uses outdated/being deprecated APIs... it still works, displaying warnings, and still good to learn the processes, but, please, someone should update that code before it stops working, and it looses reputation

By Parth J

Jun 3, 2020

A lot of information was crammed in videos of very short duration. The course could have been more comprehensive. Also, a detailed explanation of errors on making wrong submissions would be very helpful.

By Kaiwen C

Sep 25, 2022

Not in the same level as other courses in this specialization. Disappointed.

By Deleted A

Feb 17, 2020

Dataset creation task was more complex for me then all previous before.

By Shobhit G

May 16, 2020

Last Assignment does not have proper logs and instructions.

By Tim.Ding

Sep 15, 2020

Very poor course experience due to assignment grader!!

By Mark P

Apr 27, 2020

Assignments were very poor - especially week 4.

By Jinxiang R

Mar 1, 2020

week 2 and week 4 is quite hard to follow

By Sanket G

Sep 22, 2020

The final assignment is just annoying

By Triantafyllos S

Oct 17, 2020

Fix the Week 4 Assignment please.

By Ben - C L Y

Jun 26, 2020

That last week was awful.

By David N

Mar 20, 2021

This course has excellent material, and as usual for all Coursera instructors that I have experienced so far (and for Laurence Moroney specifically) the instructor is extremely qualified, sincere, engaged, and tries to pack a ton of information into his/her course.

The reason I rated this course as a two star, was in hopes that it might stand out and the two suggestions below might get heard more (as I do very much think they would help). I hope that this rating won't hurt Laurences overall rating, as I assume he has thousands of high ratings to outweigh this one rating. If it does, please change the rating to 4 star.

Hear are my two suggestions:

(1) Point 1 -- Two many screen shots with just code! I believe Laurence is trying to pack as much info as he can into the course, and I do sincerely appreciate the detail and the specific code/how-tos/answers. But after a while, it is very close to experiencing "death by power-point". Especially, since we don't get copies of these lectures after our month subscription (that is a super bummer)! There is no way we can remember all this detail anyway (without working with this stuff), or we pause and write it all down. The super detailed notebooks are wonderful and repeat all this info anyway. I would much prefer to be able to keep the notebooks (if we can after class).

My recommendation/suggestion in this case would be to at least add more animation to code slides. In Andrew Ng's presentations, he walks you through all the steps, in many cases hand-writing out the math first. This would not be practical in this course, but there are cases, where Laurence high-lights the line of code he is talking about about. Much more of that is NEEDED. In fact it would be helpful to overlay arrows (as well) that show how parameters and arguments flow from one statement to the next in the code. You might think this is not necessary (as Laurence is very detailed in his explanations and that should be sufficient). But he does talk quickly, and it would be super helpful in not getting lost, and also in breaking up the eventual monotony that can set in with too many slides of just "code and talk".

Again the material is great, but the presentation is too much repetition of code and talk/explanation. If he were to tie it together better with more highlighted boxes an arrows connecting the flow (as Andrew Ng does in his presentations), I think that (or something similar) would help a lot.

(2) Point 2 -- You may disagree, but it I believe this is very important/helpful. I am not a fan of disease based presentations when learning. I know this subject is relevant to many apps, datasets, study and research today, but when you are trying to learn a new subject, it is fun (or helpful) to have to be looking at a bunch stats on diseases, which is a subject that can engender or call up fear in the audience. Subject matter that is neutral and not related to health problems is kinder to the learner/audience (imo). I would recommend avoiding lessons and topics on subjects that can engender or call up fear during the lesson. There are plenty other datasets (that you use) that are not focus on such domains/subjects.

By Pavel

Mar 11, 2020

First three weeks were pretty interesting, especially pipelines and performance. However the examples and tasks were a little bit non-realistic. But Week 4 exercise was terrible. It is almost impossible to complete it using just grader's output. Running its copy in a separate colab's notebook is a must to be able to track errors, a lot of which are basically typos in names. The task description should have mentioned this much more explicitelly.

By Fabrice L

Mar 20, 2020

That makes me sad to give such a bad rating, because I'm a big fan of Andrew Ng and DeepLearning.ai courses, but this one is really not at standart.

The lectures are confusing, we don't understand what's the goal of all that until week3.

The assignments can be a pain to pass, not because your code is wrong, but because you added a newline or modify a bit the cell.

And overall the topic is not very interesting, in an industry setting not useful.