Chevron Left
Back to Convolutional Neural Networks

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

4.9
stars
41,304 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

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.

OA

Sep 3, 2020

Great course. Easy to understand and with very synthetized information on the most relevant topics, even though some videos repeat information due to wrong edition, everything is still understandable.

Filter by:

5201 - 5225 of 5,457 Reviews for Convolutional Neural Networks

By JiahuiWEI

Mar 14, 2019

Improve the quality of vedio please. there are too much repeats that could be easily avoided, it much worse than the first two courses, not about the centent, but the vedio itself, is your workers seriously correct the probleme of vedio??????

By Deep M

Aug 12, 2020

The course was great but only the first two weeks were sufficient for me as a Mechanical Engineer. I am not really interested in localisation and face recognition. Also, high time that you should update to Tensorflow 2.0 for your exercises.

By Piotr P

Jul 9, 2021

Great course, but assignments are from trivial to insane.

I would like to learn the ideas, but spending like 10 hours "learning" names and grammar of some packages, that will probably be outdated in next few years, is nothing fun for me.

By Marco K

Feb 17, 2018

What I really liked about the course was the actuality of the paper. However, I would have thought it absolutely necessary to explain the BackProp for CNNs. Also the grader problems in the last assignment force me to subtract two stars.

By Francesco B

Nov 30, 2017

Face recognition notebook has a bug, I passed the grader but the function triplet_loss returned the wrong value in the notebook. Several other people have had this problem despite the fact that the notebook was supposed to be updated.

By A O

May 22, 2020

Assignments do suck.

If model cannot be run locally there is no way to debug it. More test cases that would cover most common mistakes would be quite useful. Otherwise the only way through is to burry into forum topics for hours.

By Rosario C

Jan 4, 2018

The lectures were messier compared with the previous courses. Lot's of problems with the grading tools. The content of the course is great, so I would recommend it to others, modulo warning the others about being more patient :)

By Patrick S

Dec 26, 2020

This is one of the weaker courses in the specialization. I wish it had gone more in-depth. It's so far the most complex problem and I don't feel like it has gotten the same attention as the basics did, in the other courses.

By G C

Mar 24, 2018

Covers interesting material and practical problems, and tries to get the student to implement useful tools, but there is a large disconnect between the understandable theory and frameworks used to implement the solutions.

By Victor P

Nov 29, 2017

Good course, but with the conjunction of the poor quality of the Coursera interface, video quality, the price does not feel like a great bargain. Still I feel confident I can be efficient after following this course.

By Sebastiano B

Oct 21, 2019

Exercises were purposly difficult because of obscure API documentation and quirks (not because the problem itself was difficult). Good school in debugging, I personally disagreed with it (V3 if I remember correctly).

By Rob W

May 14, 2018

Enjoyed the course but the programming assignments weren't well designed I think. They were more about debugging than applying what was learned. I preferred the assignments of the earlier courses of this curricilum

By Lavínia M T

Nov 26, 2020

The Face Recognition lab just don't make any sense, the expected outputs are the ones in the Face Recognition for the Happy House. And it made the exercise very annoying! Despite it, the course is really good.

By Denys G

Dec 3, 2017

The production of the course felt rushed, there are numerous clipping issues in the videos and a major bug in one of the assignments. Also, for such a key topic to be covered in only 4 weeks felt very shallow.

By E S

Jan 21, 2018

Good explanations of the material but bugs in homework assignments and better explanations of tf usages is required for certain assignments. A refresher of tf via an additional assignment would've been nice.

By Daniel M

Jan 27, 2018

Good insights on the YOLO algorithm as well as in Siamese networks and triplet loss. Miss some more deeper understanding both in the lectures and the assignments, but I totally recommend the course anyway.

By ashwin m

Jul 22, 2019

very good topics discussed ,facial recognition and facial verification assignments do not do justice to the complexity involved.practical knowledge gained is less compared to other modules prior to this.

By Carlos V

Jul 16, 2020

The knowledge is good, and the techniques taught are valuable; however, having to use a deprecated version of TensorFlow is annoying and a lot of this will have to be re-learned to be put into practice.

By Hagay G

Apr 26, 2019

Course is very informative.

Unfortunately, unlike other courses in the spec, there were quite a few bugs in the notebooks and they took quite a while to load due to the sheer weight of the models loaded.

By David v L

Jan 2, 2018

Face recognition is a bit oversimplified, there is more to it that a simple accuracy metric. Priors are involved, which are included in the NN training, but should really be disassociated in evaluation.

By João G V

Jan 23, 2020

In contrast to course 1 and 2, I've found the videos to be rather shallow (no pun intended), in the sense that, in my opinion, they haven't explained thoroughly the techniques' underlying mathematics.

By Ramon S

Jun 20, 2021

The information in the lectures was brilliant. However, the coding assignments don't really test your understanding of the course, rather your ability to piece together the authors previous code.

By Joscha O

Jan 3, 2018

This is a very interesting and well structured but the assignments in week 4 got alot of bugs, grading gives zero points for the right ouput (according to the notebook) and ten for a wrong one...

By Swaraj L

Apr 4, 2020

The course starts normal but suddenly gets very confusing from the start of week 2. Also it gets a bit difficult to understand things later on. Otherwise its very good course and i enjoyed it

By Marcela H B

Aug 27, 2021

Overall the specialization this course is the more complex, not only regarding the main concepts I think that the assignments are hard and will be usefull have more context about tensorflow