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

40,442 ratings
5,359 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


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


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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101 - 125 of 5,334 Reviews for Convolutional Neural Networks

By Brian L

Nov 1, 2018

Great stuff! I have some background in image and signal processing and the mathematical properties of convolutions; so I it made sense to me that they would be useful in deep learning for image processing. However, that point was amplified for me when Andrew Ng showed how a convolutional layer compared to a fully connected layer: The idea that a convolutional layer was achieved through parameter sharing and masking (forcing parameters to 0) and was in a sense a form of regularization was eye-opening.

By Youdinghuan C

Jan 13, 2018

This is an amazing course. The instructor Andrew thoroughly walked through the motivation, concepts, and implementation of Convolutional Neural Network. The programming exercises are very informative, easy to follow, and helpful in terms of reviewing concepts covered lectures. Quizzes are of moderate difficulty and are also a resource for content review. Case studies chosen in lectures are very interesting and relevant. I highly recommend this course, especially for those who are new to the field.

By Michael L

Apr 29, 2020

Hardest course until now. Overall very interesting, however I think i lack some basic understanding of tensorflow concept. I would like to have more examples and explanations of it. Its just that its often unclear: this only defines the tensor, and here we evaluate it, and if I run it again, does it compute from the begging or it remembers the value, and so on. This maybe refers more to the previous course. And besides that, would be great to have some text summaries of the material. :) Thank you

By Luis E R

Apr 3, 2019

Andrew's teaching is exceptional, he finds the right way to convey the necessary information for complex concepts, he does not skip them but strikes the right balance of not going too deep, however he does warn you in a way, that you need to study them on your own.

I think the course, will give you very strong foundations if you take time to understand what you are really doing and what the algorithms are doing.

After this I think you will require a lot of practice with several examples on you own,

By Rujuta V

Aug 23, 2020

This course provides a detailed explanation of what are ConvNets. Further it also discusses real-life applications of Convolutional Neural Networks . The programming exercises which includes Face Recognition, Object Detection and Transfer Neural Networks are extremely well-designed and helps to code the above problems using tensorflow framework. I found this course extremely valuable and fun to learn and helped me a lot in improving my skills. Thanks @AndrewNg for this wonderful lecture series.

By Hari K M

Jan 18, 2018

Really good course but relatively tougher than the previous ones. Learnt a lot with best part being able to learn state of the art algorithms and implementations. Did felt kind of oblivious at times while doing the programming assignments but the discussion forums came in handy during those times. There are some issues with the grading of last programming assignment which I think will be resolved soon. I definitely recommend this course to everyone who wants to specialize in neural networks.

By Dhritiman S

Dec 9, 2017

The material in the course was very good. Andrew Ng is a fantastic instructor and is able to convey concepts in the most intuitive way.

This course would be perfect, but for the problems with the last two assignments (Face Recognition and Style Transfer). There were errors in instructions and grader solution wouldn't match solution expected in the notebook. The only way to figure out how to pass the assignments was to dig into forum posts where information was provided in a haphazard way.

By Paulo A F

Nov 9, 2017

Great course. It has all the main state-of-the-art approaches. I just missed dealing with 3D data (RGB-D and point clouds). I believe the programming assignments get better as the course progresses because they get more demanding.

This is a great overview course. I suggest anyone interested in deep learning vision to start with this course and then move on to implement a CNN in tensor flow form scratch using one of many tutorials online.

Thank to the team for this great course!

Best regards,

By Matei I

Mar 3, 2019

A lot of quality content in this course. The first half focuses on the intuition behind ConvNets and their implementation, while the second half focuses on applications. I thought that the neural style transfer application was particularly enjoyable. My only suggestion for improvement is to let the students do more work in the assignments for the last two weeks. I feel that most of the code in these assignments was black boxed, and I got to implement a minimal portion of the algorithms.

By Martin B

Sep 1, 2019

As with all the other courses by Andrew Ng, pacing and presentation are perfect. Learning this material is highly rewarding. Programming assignments are clear and accessible, although a little bit more thorough introduction in the use of Keras and Tensorflow wouldn't hurt in some cases. I found myself pretty deep in the documentation of both libraries - although that might be part of the intended learning process. Highly recommended! - Thanks to professor Ng for making this available


Jan 14, 2020

Curso excelente. Da todos los detalles más importantes sobre redes convolucionales, incluyendo las matemáticas que las hacen funcionar (incluso explica backpropagation en un ejercicio opcional) y cuáles son y cómo funcionan las aplicaciones más importantes. Omite una que otra cosa, por ejemplo cómo aplicar vectorización a todos los ejemplos de entrenamiento, y de vez en cuando durante los videos secciones de audio se repiten por alguna razón, pero mayormente está bastante completo.

By Mihai L

Feb 19, 2018

This course is still amazing. Finally understood what CNN's are for and how to use them.

This is the first time in specialization that I had to consult the forums. by far implementing in low level code convolutions (first asignment) was the most difficult part.

Spent more time then with the other courses but it was time well spent. Again Andrew NG delivers a good course.

The minor editing problems in videos are the only issue that might be raised with this course .

By Li M

May 31, 2021

I'm so suprise that the equitment was applied by this course. As the course progress continue, I found the calculation consumption amoung the coding assingment became exponentaily increase ,so I just checked the GPU inside the Coursera Jupyter Notebook, I found I'm using the Telsa V100 !! That is absolutely gorgeous especially the price of the GPU has been soaring along with the cryptocurrency.

No wonder why each epoch of the Cost function and the gradient decent can be that fast.

By Andrew K

Dec 29, 2017

The entire course is great, from the lectures by Andrew Ng, to the homework assignments, and the TA's help on the forums. The really terrible part of the course is the coursera grader, which I had to hack for 3+ hours just to pass an assignment. I dont wanna dink the review for this because the class itself is wonderful. But please fix those technical issues. So the 5 stars come from averaging 10 stars from the course itself, and 0 star for coursera technical issues. :-)

By Sergey K

Feb 1, 2021

thank you for such a comprehensive introduction to field. I cannot wait to start my own projects. I believe that wouldn't be possible without the boost given by this course.

I advice everyone interested in the field (and new to it) to take this course, this is worth and absolutely covers everything you need to know to start solving a certain kind of computer vision problems on his/her own.

appreciate everything what the Team has already done and still doing. thank you again

By Omar S M

Sep 16, 2019

This is an excellent course in which Professor Andrew Ng explains the concepts of convolution, pooling and convolutional neural networks very well. Also the various advanced convolutional network architectures and various applications in computer vision are discussed in an excellent manner along with references to the research papers on which the content is based. The programming assignments are also excellent and really help you learn the principal concepts and techniques.


Jun 2, 2019

Before taking this course, I thought computer vision had a difficult learning curve. After taking it, I found that many difficulty materials are omitted so that I could learn without too much pressure. While I could still look into algorithm details because many papers are recommended. The programming assignments cost me a little more time than the previous courses, but bring so much more fun! I felt quite proud of myself when I successfully built the CNN in my assignments.

By Meng K

Dec 31, 2020

The lectures taught by Mr Andrew are very clear and understandable, which really helps a beginner like me a lot in starting the journey to learning CNN. Besides that, the labs also provide more explanation on top of the lectures while providing the chance for students to gain practical knowledge in actually implementing the CNN. Overall, the pace of the learning is manageable, not too hard for beginners but also deep enough to really understand the workings behind CNN.

By Ashwini J

Jan 1, 2020

Thanks to Andrew Ng and team for putting together great content around Convolutional Neural Network. This is a fairly complex course, I needed to go beyond content provided in this course, specifically around understanding dimensions resulting from a convolution operation applied on an input image. This could be because it is hard to imagine a 4-d object. Otherwise, good content put together, assignments are good and useful starting point for projects in actual practice

By Shyam C N

May 17, 2020

This course was one of the better ones in the specialization. I enjoyed it very much. The assignments are a bit more practical, and require some thought while debugging. Although some TensorFlow experience from Course 2 is expected and useful, this course requires some additional reading of the TF and Keras manuals. My only suggestion to the development team would be that they improve the NST assignment's introduction of TF methods like assign() and InteractiveSession.

By Selina N

Mar 20, 2020

It's an exciting course. I find very interesting to learn object detection, facial expression and face recognition. The concept of neural style transfer is easy to understand and funny to generate image to absorb the style from another image. The explanation is useful. One improvement is some assignments only import the trained models with extra source code. It would be better for students to build by themselves to go through the whole model development step by step.

By Michael G

Mar 19, 2021

Some of the concepts in this course were at times hard to grasp. I'm still fuzzy around filtering and pooling concepts so will need to revisit. Andrew's lighthearted nature and good humor though; added levity to this otherwise fairly complex subject. My takeaway is that I have much more to learn about the subject. This class however has been a fantastic launchpad to an entirely new domain for me. I already bought some literature to dive deeper.

Thank you guys :)


Mar 7, 2021

Just wonderful! and especially unique! : we could never find such a deep and detailed course on computer vision and its applications.Terrific! (just for fun: before I always say : I need to go deeper (I have a gap to fill in computer vision), but now that's it: I went deeper than any "Inception..."(those who are going to take the course will understand the joke I just used (suspense: it concerns "Leonardo DiCaprio" ...), go take the course, without hesitation!

By Rahul K

Mar 6, 2018

Very intricately explained course! Prof. Andrew has gone the extra mile here, making sure that the basics of CNNs have been imbibed thoroughly. Kudos to the programming assignments - They're undoubtedly the toughest of all the former courses. Use the discussion forums to help get subtle hints. I now feel that I can read CNN-related papers and even work on CNN applications. Plus, you learn how to implement Neural Style Transfer (DeepDream) here!

By Chan-Se-Yeun

May 1, 2018

CNN is a tough topic to fully demonstrate. From my perspective, the lecturer simply offer an intuitive introduction and pick up some notable variant like ResNet, and illustrate the main ideas through delicately chosen case studies. That's somewhat "clever", I think. Maybe that's not appropriate, but I mean that it's friendly to a fresh learner but far from detailed and enlightening for an advanced learner. Anyway, I get to dive deeper into this field myself.