Chevron Left
Convolutional Neural Networks に戻る による Convolutional Neural Networks の受講者のレビューおよびフィードバック



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 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.



This is very intensive and wonderful course on CNN. No other course in the MOOC world can be compared to this course's capability of simplifying complex concepts and visualizing them to get intuition.


Convolutional Neural Networks: 5301 - 5325 / 5,360 レビュー

by 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.

by 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.

by 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.

by Yongseon L


by 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.

by 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.

by Joshua S


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

by Kristoffer M


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

by Prasenjit D


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

by Sandeep K C


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

by I M


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

by Shuhe W


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

by Bernard F


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

by Ryan B


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

by Coral M R


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

by Jason K


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

by Mike B


Good course but lots of technical issues with the assignments.

by Kishan


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

by Stéphane P


Videos are good, but exercises are really confusing

by chao z


content good, but assignment is in poor quality

by hossein


The structure of the assignments is not good

by Ankur S


Programming exercises have bugs

by borja v


unclear content...I'm sorry

by Alex A K


Numerous technical issues

by 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.