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

4.9
stars
42,029 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

AV

Jul 11, 2020

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

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.

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5301 - 5325 of 5,570 Reviews for Convolutional Neural Networks

By Cory N

•

Feb 2, 2020

Model implementation is abstracted in many exercises. Many helper functions are created to just make things work. TensorFlow feels a little foreign still, not enough of an overview. Higher level APIs like Keras and/or PyTorch might do better here instead of mixing in TF randomly

By Cristina B

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Feb 7, 2018

The last two weeks sometimes bored me and sometimes I had hard time in doing the assignments. The intuition behin object detection/face recognition and neural style transfer are well explained, but some more details for understaing how these models work is missing in my opinion.

By ALEXEY P

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Jun 28, 2019

The lecture content is good but the programming exercises are not explained well. Quite often you are left on your own to go through Keras and TensorFlow documentation. So, don't expect much help in learning how to implement the theoretical ideas explained in lectures.

By Jaspreet S

•

Mar 9, 2022

The course gives a high level understanding of CNN's, which is good but missing details. The content is okay. However, Andrew sounds very monotone and I happend to lose me focus very quickly in that case. Also, the many errors and corrections are confusing sometimes.

By Richard S Z

•

Apr 27, 2018

The lectures are very good. The programming assignments are sometimes infuriating and do not add to an understanding of the subject at hand. More can be done to explain the Tensorflow and Keras code. Also complete code explained line by line would be VERY helpful.

By John H

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Jun 26, 2018

I learnt a lot in this course, but i have the feeling that my knowledge is still very shallow specially when it comes to convolutional neural network design, i cannot tell pros and cons of each design and how to come up with new design that meets my use case.

By Esmaeil K G

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Dec 1, 2020

Thank you for your great course, but, to me, it has a great problem. It proposed the general theory of ConvNet and then explained some applications on ConvNet. there was nothing in between, i think it could be better if ConvNets were explained more deeply.

By Linying M

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Feb 22, 2018

The course is really good, but the assignment grader is a disaster. I spent days and nights reverse-engineering the expected codes, read the forums, only to pass the course before subscription expires, and this is certainly a very disappointing experience.

By Dushyant K

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Jul 14, 2019

I wanted to give five star; however, I could not. The function "model_nn" in Week-4. assignment -1 has been very poorly designed/ poorly explained. When I searched the forum, there are numerous questions on the same topic; but,, there was helpful hint.

By Max S

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Jan 12, 2018

A great course, but I can't give it 5 stars... There's just too many broken assignments, the videos are barely edited, staff completely ignores discussion forums, and it generally feels a little unpolished. I'm sure this will improve in the future.

By Ankit J

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Sep 12, 2020

Videos are great and give a strong understanding of the concepts, but the programming exercises are underwhelming. I don't particularly feel confident about the hands-on understanding of the concepts after complete the somewhat shallow exercises.

By JiahuiWEI

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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

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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

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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

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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

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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

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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

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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.