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

4.9
stars
42,296 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.

RS

Dec 11, 2019

Great Course Overall

One 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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4626 - 4650 of 5,610 Reviews for Convolutional Neural Networks

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

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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

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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

By Chris S

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Jan 31, 2018

Would give it 5 stars were it not for a) the grader problem in the face recognition exercise and b) some of the obscure tensorflow in the NST exercise.

But all in all prof Ng is brilliant and the way the course is set up is very intelligent and challenging.

By Ford C

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Sep 14, 2021

Subject matter is interesting and Andrew does a great job (as usual) to teach it. I do wish there would have been more practical examples in the earlier topic in order to make it easier to get a intuition for what the different network architectures do.

By Alan E

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Dec 11, 2017

It is not as detailed as previous courses, but it is a good course. I wish it would have more details abouts how to see what is doing a convNet and how to see inter-layers outputs more detailed, and also how to tune the network with conv layers. Thanks!

By Mahnaz A K

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Jul 29, 2019

Andrew makes the concepts very understandable. The way that he put assignments together helps. I wish there was more references to explain some ideas in more detail. This course has room for being expanded a little bit. Thank you for all the good work.

By Leon L

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Jul 31, 2020

Generally it is great to learn concepts related to image classification/object detection and etc. Some details of certain areas are missing, such as how to now the bx, by, bw, bh in YOLO algorithm. Have to move on to learn details from other channels.

By Mario T

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Nov 4, 2017

The video lectures are very good at outlining the concepts. The programming assignments with the jupyter notebooks are nicely done, but they do not go into much depth. Hence, the course mainly gives you theoretical knowledge than practical experience.

By Didier A

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Feb 17, 2021

This one proved to be the most challenging for me in the specialization, especially the programming assignments. While the concepts are very very well explained by Andrew, the application (though well guided) required more trial and error on my end.

By Ammar A

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Feb 26, 2021

I really liked the course but there are many tiny errors in some of the videos, which they have fixed in a following article but I got stuck in a couple videos because of those errors and later saw the article. Otherwise the course is really nice.

By Damian S

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Nov 24, 2017

Presentation of material is fantastic, but there were A LOT of technical problems with the grader that led to a lot of wasted time and frustration. Very good course, but please work to update the grader issues so future offerings are less buggy!

By Eric N

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Apr 20, 2018

This course, out of all of them, seemed to have the most grader issues. Several times I had functional code with the right answers, but it got marked wrong and I had to hunt through the discussion boards to find out how to do it the "right way".

By Saurabh R

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Nov 7, 2020

Excellent Course content and very aptly put tutorials .Course completetion is just a milestone and You keep going again and again these materials (even though you have gained certificate )you will learn something new. Thanks for this series !!

By Julien R

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Dec 30, 2017

Great course, but some discrepancy between face recognition/verification notebook and the grader make this impossible to get full grade (I had to check in the forum and enter an answer giving a result not corresponding to the expected output).

By Taavi K

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Dec 19, 2017

I really wish they didn't provide so much boiler-plate code. It seems to detract from understanding the programming assignments fully. Yes, building the whole thing yourself would take 2-3 times more effort, but the end result would be better.

By SI l

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Mar 3, 2021

Tensorflow tutorial is too short. Although i finished this course, i still dont know how to use tensorflow to build my own nn and how tensorflow actually works. This course need update the tensorflow to v2, and provide more in-depth content.

By Long N T

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Oct 22, 2020

Very nice course about a special type of Neural Network.

The course materials are really good as well as the teaching style of Andrew.

The only minor point is that the programming exercises are too easy with only "fil in the gap" challenge.

By Felipe C

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Jan 23, 2019

The course is good. Well explained.

The videos need some editing, sometimes speech is repeated which doesn't help with concentration.

Also, the forums need fixing, for someone used to Stack Overflow (and others) the forums work really bad.

By janaki r

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Dec 20, 2019

Need more quick help from discussion forum since it is very important to understand the usage and working of components in the code. The course is superb in theoretical part but I felt I needed more assistance in programming exercises.

By Marco A

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Mar 6, 2019

The contents are good, however the exercises includes too many errors and it takes too much time to read all the discussions to find out what the hack is. You should make sure the exercises are working smoothly before you publish them.

By Miguel l

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May 10, 2018

Since I have a computer vision background I was expecting much more challenges at this points when doing the pratical assignments. The explanations and intuititions about ConvNets are awesome , and this why I am giving 4 instead of 3.

By Richard C

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Feb 28, 2020

programming skills by using tensorflow and Keras are required, and learned a lot of sophisticate program structure in this tough course. Worthy! Appreciated highly, but hopefully taking programming skills before starting this course.

By Ramanand

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Sep 8, 2020

really very good course with deep knowledge of deep learning backend but some extra content and work should to added to labs for elaborated explanation and practice.

some topics like how to select model model desining were missing.

By Trong-Tin D

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Dec 4, 2017

Provide useful information in convolutional neural network and its application in image processing. However, there are many issues in the assignment and grading systems. Hope that these issues will be totally fixed in the future.