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學生對 提供的 Convolutional Neural Networks 的評價和反饋

40,120 個評分
5,312 條評論


In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more. By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data. 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....



Great Course Overall\n\nOne thing is that some videos are not edited properly so Andrew repeats the same thing, again and again, other than that great and simple explanation of such complicated tasks.


I really enjoyed this course, it would be awesome to see al least one training example using GPU (maybe in Google Colab since not everyone owns one) so we could train the deepest networks from scratch


5226 - Convolutional Neural Networks 的 5250 個評論(共 5,284 個)

創建者 Aman B


Programming part was not explained well. I guess programming syntax and flow of code should be explained too instead of just telling theory or focusing mainly on theory.

創建者 Daryl V D


TOO MANY BUGS IN THE EXERCISES.It was a dis-incentive. Really.And I love me some! It has been great. The videos and content structure are fantastic.

創建者 Arsh P


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.

創建者 Yongseon L


創建者 mike v


The content is excellent, but there were technical problems with the final homework assignment that were not addressed by staff in a timely manner.

創建者 Sébastien C


Content was interestind and provided good theoretical overview. Exercices where you just have to fill in some line of codes are not usefull.

創建者 Joshua S


Some of the code was incorrect and the guidance was often confusing. Visibly worse than the other courses in the specialization,

創建者 Kristoffer M


Don't feel like I understand these models much better than before. Still don't see the logic of the identity layers

創建者 Prasenjit D


Lots of problem with the grader. Wasted a lot of time grappling with grader issues. Very disappointed.

創建者 Sandeep K C


The quality of some of the graders e.g. IOU is poor. One cannot make out what exactly is it checking

創建者 Igor M


Disappointed by the quality of notebooks, which often disconnect and lose all the code you wrote.

創建者 Shuhe W


The course assignment parts have many errors, I have to fix it myself. That's silly.

創建者 Bernard F


Good content, but quite a bit of technical work is needed to present this better.

創建者 Ryan B


for goodness sake "your didn't pass the test" isn't feedback for notebook grades

創建者 Coral M R


Dificultades en la hoja de tareas de Face Recognition que deberían solucionar

創建者 Jason K


The content was good, as usual, but week 4's quiz was pretty buggy.

創建者 Mike B


Good course but lots of technical issues with the assignments.

創建者 Kishan


The notebooks were too simple. And the grader was not working.

創建者 Stéphane P


Videos are good, but exercises are really confusing

創建者 chao z


content good, but assignment is in poor quality

創建者 hossein


The structure of the assignments is not good

創建者 Ankur S


Programming exercises have bugs

創建者 borja v


unclear content...I'm sorry

創建者 Alex A K


Numerous technical issues

創建者 Mostafa A


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.