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Learner Reviews & Feedback for Convolutional Neural Networks by DeepLearning.AI

4.9
stars
42,149 ratings

About the Course

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....

Top reviews

SH

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Great content in lectures! Automatic graders for programming assignments can be tricky, and set to old versions of tf sometimes, but answers to these issues are readily found in the discussion forums.

FH

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Amazing! Feels like AI is getting tamed in my hands. Course lectures , assignments are excellent. To those who are not well versed with python - numpy and tensorflow , it would be better to brush up.

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5526 - 5550 of 5,583 Reviews for Convolutional Neural Networks

By Yongseon L

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Jun 15, 2019

https://www.coursera.org/learn/convolutional-neural-networks/programming/IaknP/face-recognition-for-the-happy-house/discussions/threads/NcpP7i95EemJswr-eOHMNg

By mike v

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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.

By Sébastien C

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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.

By Joshua S

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Nov 29, 2019

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

By Kristoffer M

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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

By Prasenjit D

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Dec 6, 2017

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

By Sandeep K C

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Dec 28, 2018

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

By I M

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Oct 17, 2019

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

By Shuhe W

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Jun 8, 2019

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

By Bernard F

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Dec 13, 2017

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

By Ryan B

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Jan 2, 2020

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

By Coral M R

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Jun 7, 2019

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

By Jason K

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Dec 13, 2017

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

By Deleted A

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May 7, 2018

Good course but lots of technical issues with the assignments.

By Kishan M

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Feb 13, 2018

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

By Stéphane P

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Mar 30, 2019

Videos are good, but exercises are really confusing

By chao z

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Feb 22, 2018

content good, but assignment is in poor quality

By hossein

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Jul 19, 2020

The structure of the assignments is not good

By Ankur S

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Dec 30, 2019

Programming exercises have bugs

By borja v

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Aug 22, 2019

unclear content...I'm sorry

By Alex A K

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Sep 28, 2019

Numerous technical issues

By Mostafa A

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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.

By Matteo V

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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.

By Martin B

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Dec 30, 2017

Lectures were good, but the assignments have major problems with the grading. On several problems, you have to put in an incorrect solution in order for the grader to accept it. This have been reported by a number of students in the forum. It needs to be cleaned up.

By Ed G

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Feb 7, 2021

This course is much poorer than the previous courses in this series. Much of the content was at a very high level without sufficient detail. More explanation to make a concept clear was lacking. Hope for some improvement to the content for future learners.