学生对 DeepLearning.AI 提供的 Convolutional Neural Networks 的评价和反馈
课程概述
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FT
Apr 6, 2021
Very good introduction to programming convolutional neural networks. Although the models and functions needed are complicated ,this course takes you by the hand and introduces to all these wild ideas
AG
Jan 12, 2019
Great course for kickoff into the world of CNN's. Gives a nice overview of existing architectures and certain applications of CNN's as well as giving some solid background in how they work internally.
5576 - Convolutional Neural Networks 的 5600 个评论(共 5,639 个)
创建者 Arsh P
•Dec 15, 2018
Though the videos were very good but the assignments require too much from us and also there are few mistakes in week 3 and 4 notebooks which take a lot of time.
创建者 Yongseon L
•Jun 15, 2019
https://hua.dididi.sbs/learn/convolutional-neural-networks/programming/IaknP/face-recognition-for-the-happy-house/discussions/threads/NcpP7i95EemJswr-eOHMNg
创建者 mike v
•Jun 8, 2019
The content is excellent, but there were technical problems with the final homework assignment that were not addressed by staff in a timely manner.
创建者 Sébastien C
•Aug 18, 2020
Content was interestind and provided good theoretical overview. Exercices where you just have to fill in some line of codes are not usefull.
创建者 Joshua S
•Nov 29, 2019
Some of the code was incorrect and the guidance was often confusing. Visibly worse than the other courses in the specialization,
创建者 Kristoffer M
•Nov 30, 2019
Don't feel like I understand these models much better than before. Still don't see the logic of the identity layers
创建者 Prasenjit D
•Dec 6, 2017
Lots of problem with the grader. Wasted a lot of time grappling with grader issues. Very disappointed.
创建者 Sandeep K C
•Dec 27, 2018
The quality of some of the graders e.g. IOU is poor. One cannot make out what exactly is it checking
创建者 I M
•Oct 17, 2019
Disappointed by the quality of notebooks, which often disconnect and lose all the code you wrote.
创建者 Shuhe W
•Jun 8, 2019
The course assignment parts have many errors, I have to fix it myself. That's silly.
创建者 Bernard F
•Dec 13, 2017
Good content, but quite a bit of technical work is needed to present this better.
创建者 Ryan B
•Jan 2, 2020
for goodness sake "your didn't pass the test" isn't feedback for notebook grades
创建者 Coral M R
•Jun 7, 2019
Dificultades en la hoja de tareas de Face Recognition que deberían solucionar
创建者 Jason K
•Dec 13, 2017
The content was good, as usual, but week 4's quiz was pretty buggy.
创建者 Deleted A
•May 7, 2018
Good course but lots of technical issues with the assignments.
创建者 Kishan M
•Feb 13, 2018
The notebooks were too simple. And the grader was not working.
创建者 Stéphane P
•Mar 30, 2019
Videos are good, but exercises are really confusing
创建者 chao z
•Feb 22, 2018
content good, but assignment is in poor quality
创建者 hossein
•Jul 19, 2020
The structure of the assignments is not good
创建者 Ankur S
•Dec 30, 2019
Programming exercises have bugs
创建者 borja v
•Aug 22, 2019
unclear content...I'm sorry
创建者 Rick C
•Mar 20, 2025
Although I like the material, the assignments don't work. Twice now, I have completed the Jupyter notebooks with everything working and when I submit to the automatic grader I get a zero. The error sheet just gives cryptic messages. When I try to get help, I just get messages saying that there is nobody overseeing this course. I'm sorry to give it only 1 star but actually I would give it 0.
创建者 Mostafa A
•Dec 16, 2017
Assignement: Face recognition for happy house was not happy at all
it took me 4 attempts to pass.
triplet_loss function you need to submit incorrect answer to pass. to get correct answer you need to have axis=-1. Bu to pass you have to take it out.
I hope you guys fix to stop more people to waste there time.
Not happy at all.
创建者 Matteo V
•Jun 6, 2021
I took the basic ML course and now am taking all the Deep Learning courses. This is by far the worse course so far. Assignments are very unclear. Even explanations are less linear than in previous courses. Support is now on a different platform and not directly on Coursera. I would give it a negative grade if I could.