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

4.9
stars
42,065 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

RK

Sep 1, 2019

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.

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.

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4576 - 4600 of 5,574 Reviews for Convolutional Neural Networks

By Scott A

•

Nov 27, 2018

The course is an excellent introduction to convolutional neural networks. As always, in limited time, there is a trade off between depth and breadth. What content and assignments there were were excellent. Probably the only reason why I rated the course with 4/5 stars is that I wanted more.

By Adrianus B K

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Nov 25, 2018

This course touch many state of the art deep learning networks especially in image processing. The programming assignment is more challenging to me mainly because I am not that familiar with tensor flow, and higher number of dimension in this field requires more focus and concentration.

By daniele r

•

Jul 15, 2019

Overall it was a positive experience. I expected a little bit more by assignements and by hands-on work in general. I have passed all the grades, but I am still confused about the functioning of tensorflow and why sometimes the assignements stick to Keras while sometimes use tensorflow

By Gustavo S

•

Mar 1, 2018

Andrew Ng covers relevant and current topics on DeepLearning community, autonomous driving, face recognition and convolutional neural networks. Challenging assignments, and well-balanced quizzes.

Could present the hyperlinks to DeepLearning whitepapers and articles as a course resource.

By Mehdi

•

Nov 17, 2017

It is great to play with state-of-the-art neural network algorithms and architectures, but it is a shame that the programming assignments did not involve training/optimization, even on small datasets, or pretrained one (in the case of hyperparameters tuning).

Otherwise, great course !

By Changbin D

•

Mar 8, 2018

The lecture is very good, and provided the most recent development in this field.

But the homework, most of time, I am searching the forum try to understand the tensor flow, also the errors from the grading system still exists from time to time.

Overall, this is a very good course.

By Marc S O

•

Aug 17, 2020

very intuitive and guided. encourages students not to be intimidated by research papers. promotes open-source software and learning weights. I would've given it 5 stars if it used the latest version of Tensorflow (it seems to be old since it is still not using the eager execution)

By XIAO X

•

Dec 16, 2017

The 2nd coding assignment in this course has a bug, in triplet_cost, the expected output is the correct answer but when my answer matches it I cannot pass, the previous versions (v2) give wrong expected answer and in v3 in order to pass I have to match on that. Please correct it.

By Achal J

•

Aug 10, 2020

This course is awesome, just like the other course, but this course required more perseverance and more understanding and more hard work.

Take this course should only be taken if you are thoroughly prepared for it. The previous 3 courses

are quite important for the course.

By Marco M

•

Jan 14, 2018

The course provides a good overview on the most famous techniques for CNN. However, there are several errors in the assignments not solved yet that make you to loose a lot of time. Moreover, it is very hard to do the latter if you do not take all the courses in sequence.

By Kees v d T

•

Mar 13, 2020

The theory behind convolutional neural networks is very well explained in the video's. Also the programming assigments help you understand CNN a lot better. However, before this course you should have had a course in tensorflow / python. Because the exact syntax is hard.

By Giorgia M

•

Jan 7, 2019

very helpful and interesting! the only drawback in my opinion is that one who's not trained in using tensor flow can have hard times in figuring out what's happening and what should be done.

I would say knowing TF it's a prerequirement to fully understand everything.

By Pat S

•

May 21, 2020

some duplication during the video, and some differences between the course work and the assignment which makes it difficult. Particularly struggled during coding phases as it assumed a greater level of python \ tensorflow skills than outlined in the pre-requisites

By Edward M

•

Dec 29, 2019

This course was definitely harder than the ones before it in this specialization so I know I will need to review this a few times more. But overall, a great intro and insight into how this very sophisticated image recognition and generation scenarios work. Thanks!

By Alireza N

•

May 20, 2022

It was an all-encomaping course and thanks a lot for providing us with such quality tasks and materials. Just a start for those of us who are trying to figure out what they want and also those who are planning to get choose what topic to dig deeper in the future.

By Filip A

•

Feb 13, 2019

Some things seemed a bit unfinished, some of the videos weren't edited properly. On several occasions Andrew said the same thing over again, like a retake. The code was also somewhat buggy, but it seems they're on it.

The course in itself and it's content is great!

By Jonathan

•

Mar 6, 2019

Great course on the theory side - well explained.

Programming side is not trivial unless you have in depth relevant experience in these types of apps. Programming support if very weak (unrealistic) - but there are treasures in the forum (good search capabilities).

By Keisuke I

•

Apr 13, 2018

Another great course by Andrew Ng. There are quite a few bits in the video that were clearly meant to be edited out, and also some quiz and homework grading have errors. But nothing can be perfect, and I really don't know any other places to learn Neronet online.

By Sajal C

•

Mar 27, 2020

Its one of the tough course of this specialisation. Algorithms will not be covered much in depth however implementation details are very clearly explained. You can of course read the in-depth details from the references of the research paper given in the slides.

By Timo K

•

Sep 29, 2018

Excellent course once again. There were some nuances in the programming exercises that included some time consuming overhead for troubleshooting (thus 4 stars). Nevertheless, would highly recommend to anyone aiming to dive deeper into convolutional neural nets.

By Rob B

•

May 15, 2020

Thanks again, another useful and well thought-through course. Some tidying of the videos and checking of the scripts instructions (especially non-mandatory steps) would polish this course. Overall it flows well and contains very useful insight and intuition.

By Saptashwa B (

•

Sep 8, 2019

Great course for beginners but disengagement of the tutors for some concept related questions and very small number of typos on the slides are little drawbacks. But definitely encourage everyone to take this course for getting the building blocks of CNN right.

By Vimal K N

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May 7, 2018

Concept of anchor boxes was not clear. Hopefully, you can add some more clarification for future students. How are anchor box dimensions determined? What happens to objects that are near the camera vs those that are far away? Do anchor boxes scale accordingly?

By Bart-Jan V

•

May 2, 2018

Learned a lot, obviously, but felt like I had to look for answers more actively than previous courses. Obviously, the positive side is that you get trained at debugging as well and at searching the internet. Being self-taught anyway this somehow felt familiar.

By justin g

•

Apr 16, 2018

grading for assignments seems buggy, and mismatched to hints on the discussion forums.

Slight differences in an arg to a function can result in a correct result in notebook but failing assignment. This was un-intuitive and confusing. Content was good otherwise