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.
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
By Chris M•
Aug 2, 2019
The assignments are less copy paste and some allow the student to explorer different NN architectures. However, most of the videos are still a waste of time. And the methods needed to complete the assignments aren't taught to the student. Instead you have to spend a lot of time searching and hoping you find the right method.
By André N•
Dec 2, 2018
video courses were really good, but the programming assignments drove me nuts. I am a senior software developer and I am writing software for more than 10 years now. I had a really hard time understanding the Tensorflow code. I think it is better to suggest a student to learn the basics of Tensorflow before doing this course
By Tuấn T L•
Dec 22, 2021
This course is well organized with CNN knowledge. However, it seems like the team is overwhelmed to maintain both a big Tensoflow tech stack in programming assignments while keeping academic science core concepts. Some code comments are outdated and the mentors definitely can not follow up all the issues raised by students.
By Mladen M•
Jan 20, 2020
Couple of suggestions: 1) fix the artwork via neural networks assignment as there is a bug in your code 2) With the lectures I would suggest that you do a summary explanation of how the whole process works (all steps and motivation - a review) at the end of each group of lectures (one for artwork one for face recognition)
By Cristian G•
Oct 13, 2022
I think this course lacks hands-on experience, and for that I think it should improve the labs, people would learn much more from a youtube-kind tutorial than from these "#your code starts here / #your code ends here"-labs.
The quiz interface is horrible, i need to refresh my browser many times before it loads properly.
By Do Q B•
Jun 11, 2018
The theory is very good but the exercise part is not good enough for me (For example in the Face Recognition exercise, I'd like to build (even a simple model) and train the triplet loss function... However, all that I can do is only loaded a trained model and then apply some simple similarity measure on encoding vector)
By Zach L•
Apr 9, 2018
videos are excellent and insightful as always. I thought the homework assignments for this section were the worst yet. simultaneously holding your hand so much you don’t do or learn anything meaningful, and also providing you with obscure or insufficient guidance in the moments when you’re asked to fill in the blanks.
By Ernesto G d l P•
Apr 11, 2021
There is major room for improvement on the automatic grader, under some particular cases, the answers are correct but the grader will give you zero with no feedback (in my case, I made a mistake with declaring local variables as global in the code). This issue is quite frustrating, the forums helped a lot though.
By Anthony M•
Dec 4, 2017
Great class and amazing assignments. I really enjoyed learning about CNNs, YOLO, and Neural Style Transfer.
Errors with submitting the assignments, particularly weeks 2 & 4 took away considerably from the overall satisfaction with the course.
Thank you once again for providing a rich learning environment. :)
By Christopher C•
Sep 9, 2020
Programming assignments were not to the level of the prior courses in the series. Should have more illustration of using Keras/Tensorflow. Assignments either were too spoon fed or there was too little reference information whereas prior courses had a good balance. Many of the keras links are dead.
By Luis F A•
Jun 9, 2019
Theoretical content was very informative and high quality. However, some problems with the programming assigments were annoying. For instance, for the last programming assigment some weights would not load and it was necessary to go get the weights from the github repository of some other person.
By Shreyash W•
Jan 6, 2020
The week 1 and 2 were perfect, then week3,4 had some issues with the lectures- Andrew sir was repeating some parts and the problems/corrections in the slides.Also the week3 object detection was tough n the hints were not enough, with the errors in the assignment submission costing me a day
By Achille H•
Jul 6, 2020
Great content, veerything is clear and concise. Only downside is the grading of the exercises, which sometimes requires you to use a very specific syntax (even though another syntax gives the exact same results) and causes hours of painful debugging and reading through the forums.
By Cory N•
Feb 2, 2020
Model implementation is abstracted in many exercises. Many helper functions are created to just make things work. TensorFlow feels a little foreign still, not enough of an overview. Higher level APIs like Keras and/or PyTorch might do better here instead of mixing in TF randomly
By Cristina B•
Feb 7, 2018
The last two weeks sometimes bored me and sometimes I had hard time in doing the assignments. The intuition behin object detection/face recognition and neural style transfer are well explained, but some more details for understaing how these models work is missing in my opinion.
By ALEXEY P•
Jun 28, 2019
The lecture content is good but the programming exercises are not explained well. Quite often you are left on your own to go through Keras and TensorFlow documentation. So, don't expect much help in learning how to implement the theoretical ideas explained in lectures.
By Jaspreet S•
Mar 9, 2022
The course gives a high level understanding of CNN's, which is good but missing details. The content is okay. However, Andrew sounds very monotone and I happend to lose me focus very quickly in that case. Also, the many errors and corrections are confusing sometimes.
By Richard S Z•
Apr 27, 2018
The lectures are very good. The programming assignments are sometimes infuriating and do not add to an understanding of the subject at hand. More can be done to explain the Tensorflow and Keras code. Also complete code explained line by line would be VERY helpful.
Jun 26, 2018
I learnt a lot in this course, but i have the feeling that my knowledge is still very shallow specially when it comes to convolutional neural network design, i cannot tell pros and cons of each design and how to come up with new design that meets my use case.
By Esmaeil K G•
Dec 1, 2020
Thank you for your great course, but, to me, it has a great problem. It proposed the general theory of ConvNet and then explained some applications on ConvNet. there was nothing in between, i think it could be better if ConvNets were explained more deeply.
By Linying M•
Feb 22, 2018
The course is really good, but the assignment grader is a disaster. I spent days and nights reverse-engineering the expected codes, read the forums, only to pass the course before subscription expires, and this is certainly a very disappointing experience.
By Dushyant K•
Jul 14, 2019
I wanted to give five star; however, I could not. The function "model_nn" in Week-4. assignment -1 has been very poorly designed/ poorly explained. When I searched the forum, there are numerous questions on the same topic; but,, there was helpful hint.
By Sambit M•
Jun 1, 2019
Bugs in the template code cause a lot of time waste.
Also, the exercises need to be better which teach how to actually build a model ground up rather than just filling in small parts.
Getting the main models working is the key, which is not covered here.
By Max S•
Jan 12, 2018
A great course, but I can't give it 5 stars... There's just too many broken assignments, the videos are barely edited, staff completely ignores discussion forums, and it generally feels a little unpolished. I'm sure this will improve in the future.
By Ankit J•
Sep 12, 2020
Videos are great and give a strong understanding of the concepts, but the programming exercises are underwhelming. I don't particularly feel confident about the hands-on understanding of the concepts after complete the somewhat shallow exercises.