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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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4526 - 4550 of 5,570 Reviews for Convolutional Neural Networks

By Tanish G

•

Sep 7, 2019

The lectures were outstanding for understanding the theory but the programming assignments had almost everything pre implemented. I understand that these algorithms require a very long implementation but that just made the programming assignment as just filling the blanks and didn't make me capable of writing code for these algorithms on my own.

By Eero L

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May 13, 2019

I would very much like to give 5 stars, but because there are mistakes in the quizzes as well was in the assignments which are not corrected, I must decrease this rating by one star. I don't find it very appealing that I must find answers for quiz bugs from the discussion board.

So please, update the material whenever mistakes and bugs are found.

By Robert K

•

Dec 12, 2017

Course is amazing, teaches you a lot for ConvNets, image recognition, verification, building simple models in a couple of minutes, and refining them. The only drawback is that there are errors here and there, but fortunately they are being addressed, so future learners might experience less problems. Even with this, it was a really nice course.

By mayur n

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May 10, 2018

Absolutely fantastic course,I just loved it.....Only problem was to me in the face recognition topic.

Training a siamese network need sharing base model with multiple inputs,which is important for training model with unconventional loss functions like triplet loss.And this isn't covered in the course.If that is included in it would be awesome

By Daan v d M

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Sep 4, 2020

Again an excellent course. Great insights in convolutional networks. The programming exercises should use ore recent TensorFlow version as the functions cannot always be found anymore in the documentation, making the tf exercises hard to make - and at a certain moment it becomes a bit trial and error instead of a result of logical thinking.

By Maulik S

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Aug 3, 2020

Very informative and helpful to understand the fundamentals.

The exercises could have been designed better to understand TensorFlow, while one or two more exercises for the framework could have helped improved understanding of the framework. Also, several exercises feel like being spoon-fed and do not add much to the knowledge about CNNs.

By Binil K

•

Mar 2, 2018

Nice Course and it covers lot of details about the current concepts in deep learning. A little more details into YOLO and NST which included how we can train them ourself instead of using a pretrained model would have been better. A little more details about tensorflow and keras implementation of the algorithms could make it more helpful.

By Narasimhan, S

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Jul 25, 2020

Overall a good course in understanding some of the concepts in the world of Detection of Images, Identifying them and building a fence around them to see what are their dimensions and how to ensure we dont fail to identify them and also identify humans which is becoming more and more prevalent in more and more countries across the world.

By Egnatious P

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Apr 25, 2020

Week 3 of the course was a bit tough, well for two reasons. Firstly I thought the exercises had long explanations and too much detail which really needed much attention to retain the key information and be able to apply it. Secondly, I have been doing this non-stop for the last couple of weeks so at this stage i think I'm also exhausted.

By Frammery H

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Jul 16, 2019

Extremely interesting bit a bit too high level compared to the 3 previous ones. Convolutional Networks usage are well described but the technical implementation from scratch is incomplete which makes us dependent on tools such as tensorflow or keras. An additional video showing the maths behind the complete backprop would be a real plus.

By mitch d

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Jun 25, 2018

Would have liked more explicit math, maybe as optional material, for some of the "you don't need to understand the math" parts. Also, there were some errors / inconsistencies in a couple lectures. (See the forums for more info.)Overall, though, a very good course - and much "meatier" than some of the ones that preceded it in this series.

By Jeff K

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Dec 11, 2017

The facial recognition and verification stuff was pretty cool and I'm glad it was included. I wish the python grader was implemented correctly and there were some technical difficulties with the Jupyter notebooks. The course opened later than expected which made me lose a month's worth of fees before a notice was finally sent out. :(

By Ziad A

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Sep 9, 2020

This course is amazing, but it just needs to be split up into a bigger number of weeks, have much more quizzes, like every 3 videos there's a quiz, but the problem is that you have to watch about 2 hours straight until you reach that quiz, it is hard to maintain your focus abilities in these circumstances. Overall rating? Amazing!

By Hamza A

•

Nov 24, 2017

Awesome course ! I got to understand how some awesome applications like neural style transfer really work, implement the resnet 50 which is state of the art and most important I learned how to know which feature of an image an individual neuron in a given layer actually learns ! Thank you all for the effort, you did a great job !

By Mihir N

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Dec 28, 2017

Liked first three weeks but not the fourth one. There are couple of reasons why I didn't like much 1) car detection totally failed when i tested with my images. Images were clear and with proper angle (as if taken from dashboard camera). 2) face recognition assignment wasted lots of time due to incorrect data and expected result!

By Aditya K

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Oct 24, 2019

It was a good course and Andrew did a fantastic job of explaining all the concepts in an accessible manner. I do wish he had gone more in depth about backpropagation and I also felt that the assignments towards the end were dumbed down and hence I don't really feel like I have as good a grasp on the topics as I would have liked.

By Lim K Z

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Jan 15, 2019

I think there is an error in the assignment for neural style transfer. My code was correct and was also graded correct, but one of the expected output - total cost was wrong. Wasted a lot of time searching for the cause. On the whole the course was still great. Like Andrew's enthusiasm and lots of examples from the industry.

By Ghanshyam

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Jul 7, 2018

This course really gave feel of deep learning. The way Andrew Ng taught and content of course is preciously valuable. star less because the language Keras and Tensor Flow where I felt difficulty. You should provide language study material with examples. I hope you would implement it. It will help to work with language with ease.

By Aleksandra C

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Jan 31, 2018

The content of the lectures is great, introducing state-of-the-art solutions in a way everyone can understand. However the grader for the assignments needs fixing. Many times I had a correct solution, spent a lot of time trying to fix it only to discover via the discussion forum that an incorrect solution passes the grader ;/

By Sandeep P

•

Jun 24, 2018

A great introduction to convolutional networks. Highly recommended to learn about the cutting edge scale and depth. Minor suggestion: The Jupiter interface seems to be buggy on chrome (mac). Doing a save and check point can help relieve this problem (at least re-coding wont be needed). It would be nice if a fix is available.

By Deleted A

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Aug 22, 2020

Excellent videos and quizzes to learn or review CNN concepts.

The notebooks however should be refresh using modern frameworks (Tensorflow2 or Pytorch). The explanations inside are great, but there are also too guided: more freedom should be given to implement methods with only rigid formats regarding submission evaluation.

By Katya M

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Mar 9, 2018

Excellent as usual lectures from Anrdew Ng. But at some videos some key points are missed like Neuro Style transfer is transfer learning and we use pre-trained CNN. It would be good to have some data flow drawing. YOu get it later from exercise but not from video at once. Exersises are less accurate than previous courses.

By udit R

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Jun 1, 2023

It was my first specialisation course , i felt great and learn lot from it , i also get a good hang of CNN and CV from basic to core , the only downside was the assessment system it was too confusing at first and i still have not full confidence in building great convolutional models but its a great start for any amateur

By Rahul K

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Jan 15, 2019

The course does cover fair amounts of basics very well. But the course content just provides a starting point and I feel it lacks depth. The course assignments are nice and give a good platform to implement what was learned in that week but it was more of filling in the blanks rather than building a full-fledged system.

By XING Z

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Feb 3, 2018

Feel this course's assignment is not quite heavy on why people build the CNN that way. But it tells how the thing is. I would like a "Future work" section to give some inception on the future of CNN and limitation in terms of image orientation, content, what the deep learning cannot solve right now for image based work.