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

4.9
stars
39,870 ratings
5,270 reviews

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.

RS
Dec 11, 2019

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.

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5126 - 5150 of 5,243 Reviews for Convolutional Neural Networks

By eric v

Apr 19, 2018

some of the quizzes were a little buggy

By Walid M A

Nov 17, 2017

I did not like the assignments of w#4

By Pakhapoom S

Mar 14, 2021

The videos need to be edited properly.

By sai d s

Jan 17, 2019

Little bit hard programming Excercise

By Xirui Z

Apr 7, 2021

Too hard for someone new to tf.

By Sanskar j

Jun 18, 2020

Assignments can be made better

By Jisheng L

Jun 15, 2018

Need improvement on assignment

By Pedro C

Jun 10, 2018

notebook were not functional

By Modassir A

May 11, 2020

need improvement of content

By Olatunji O

Feb 12, 2019

Notebooks are a bit buggy

By Yi-Hao K

Jan 20, 2018

Serious bug in assignment

By Yide Z

Jan 13, 2018

too many errors in test

By akshat

Jun 25, 2021

Labs should be tougher

By KevinZhou

May 8, 2018

部分内容讲的不是很清楚,有些剪切不好,有重复

By Kenneth C V

Dec 4, 2020

Very complex Subject

By zz

Mar 5, 2018

没有翻译 tenserflow也讲得不好

By Pavao S

Mar 2, 2018

Not enough theory

By neda m

Jun 22, 2020

too theoretical

By Volker H

Dec 16, 2017

too many bugs

By Shimaa

Aug 30, 2021

so hard :(

By Logos

Aug 27, 2020

It was okay. Andrew is obviously very knowledgeable, and there is a wealth of knowledge here. I could go through it a couple more times and still pick up new stuff.

That being said, I've heard him mention he did these videos at like 1 or 2 in the morning after work, and it's very obvious from the videos. He makes so many mistakes that every other lecture (it seems like) has a **CORRECTION** notification next to it. I mean it's great they give this additional correction information, but it would be even better if you just redid the video.

Furthermore, he like stops in the middle of the videos and then repeats the last sentence he said, because he made another mistake. I get it, Andrew is very successful, he's very busy, and I am definitely grateful for the knowledge he's provided in this course. But this makes for a very poor learning experience, because I'm taking notes, and I have to go back and redo them, plus the general angst you get when you're learning something and someone's like "oh wait nope that's not right, forget that." Well for God's sake I already learned it.

Finally, the submission assignments are the most annoying things I have ever come across. They are riddled with errors and misguided information where they literally tell you to use the wrong parameters, and then they never fix it. You have to go into the discussions to find out why your code is wrong, even though you're doing it right.

Then, you'll get everything right on your code for the test cases, and when you go to submit it fails you. And when I say it fails you, it gives you a literally 0 out of like 30 points. And the grader output just says "your submission was incorrect" like no way, I had no idea. Thank you for that very **cough** helpful piece of info.

If you go to the discussions, you find out this is actually a problem with how the grader is built, because if you don't format your code exactly the right way, it fails you, even if your solution is correct. I don't understand why it can be right when you run test cases, but submitting it fails.

Overall, I give it 3 stars before the poor grading, but because of the poor grading performance I have to bring it down to 2. I can't tell you how much time I wasted trying to figure out why my code was wrong just to realize it was right, but they screwed up their implementation.

In conclusion, this reminded me of a college course, where the professor has a ton of knowledge and is in high demand, and doesn't really care whether you get anything out of the course or not. It's sloppy, doesn't seem to be maintained very well, and most of the mentor's responses are literally "did you look at your colleagues similar questions?" Like no I didn't, that's why I'm asking. Why am I paying you so I can spend more time debugging your screw ups? Or maybe I did and I still don't get it because your explanations are ridiculously unclear.

I have one more course in this specialization and I absolutely can't wait for it to get over with so i can move on to more productive (and immersive, since these exercises are just one off "do this then do that" instructions, I still don't know how to set up a Deep Learning project from scratch) ways to learn Deep Learning. If Andrew wasn't so knowledgeable about this topic, I wouldn't even take it because it's that bad. But really you can't get this type of knowledge in such a condensed form anywhere else.

By Juan R

Feb 15, 2018

I found it very easy to go through the assignments and the quizzes were great, but I do have 2 complaints: -- I didn't get quiz feedbacks (they seem to be disabled), so, this is a huge let down and I wasn't able to completely grasp the concepts. -- For example the Gram matrix I had to accept it was true when they said "if the filters are quite similar then the dot product will be high". Show this please? #mastery #selfcontained. -- Another example, on the programming assignment, on Neural Style transfer, it is POORLY explained how the framework works when it comes to setting a_G and a_C. Then it is said "this will be covered (explained) in the "model" function, which wasn't. -- I have printed most of the mentioned papers and I am starting to read them, I loved the fact you recommended papers on this lesson, and the rest of the programming assignments were great, especially when you would provide "Hint" to go to the docs and lookup the method, etc.

By Jeff N

Apr 12, 2018

I feel this is by far the weakest of the first 4 courses in the series. The information is really valuable and the homework offers almost no opportunities to actually explore CNN architectures. The homework is more about implementing a few parts of a dictated network where all of the critical information is provided. The only exercises are in more vector manipulation and knowledge of frameworks that are never talked about in the actual course material. I'd love real framework material and real opportunities to practice using them, but the limited exposure here does not cut it.

Basically, I listened to the videos talk about CNNs, answered quiz questions about minor foot notes in the lectures, and then messed with vectors again. Oh, and the video editing was pretty choppy in this course compared to the others. Disappointed.

By Thomas D

Oct 10, 2020

The material covered in the course is very good but the instructors really need to go back over the course materials (particularly the homeworks) and clean them up. Many of the links to the TensorFlow documents are out of date and link to missing information. These aren't necessarily updated in the forums either, which do not seem to have much of a TA presence anymore. It would be nice if the lectures & slides could be updated to incorporate the errata in the syllabus but I understand that could be a lot of work. However, it seems like it would be better to present the errata before the lectures in the syllabus. Admittedly its a small complaint but it seems like an easy fix and the fact that it hasn't been done says something about the amount of care put into maintaining the course.

By Alexandre E

Dec 4, 2017

Course is great, but there were several bug in the homework, including misleading tests.

In one, getting the right value (triplet loss) results in a failing grade, getting the wrong values (using help from the forum) get you to pass the test. In another test, there were corrupted files; one has to add a print statement in a helper function, learn what file is corrupted, rename it, reload the exercise, and voila, it works.

Clearly, graders should survey the forum more closely to address these issues. Hopefully it will be addressed soon, and these comments will become moot.

That aside, the quality of the videos and the insight provided by Andrew Ng are second to none, thanks for the outstanding instruction