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.
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 Abhishek R•
The course material is really good and Andrew explains things really well. However, the programming assignments cause a lot of problem owing to the performance of the grader where by correct answers are marked as incorrect/incomplete and the only option to submit the assignment OR get it graded correctly is to follow steps from the forums to make changes to the files to trick the grader in order to get it submitted. From the forums it seems like these problems have been there for over 2 years and still has not been fixed. Overall the programming assignments are really good and helps in understanding the implementations.
by Adi G•
I was taking this course because I hope to apply machine learning to biological problems. So while the first two weeks were great and super general, the third and particularly the fourth weeks were less relevant to me at this point but I had to struggle with them to get the certificate. Ideally, I would say that a way to improve this would be to create another week, dedicated either to biological problems or to something more general to all and let the students choose between the content of that week vs. the current 4th week. Another option is to make this a 3-week course and leave the 4th week entirely optional.
by Vincent S•
The video lessons gives very clear and understandable concepts but I didn't feel that the coding exercises will help me to write my owns. I could easy fill in the blanks and get the required grades but I have to admit that for the most of it I didn't understand what I was doing or what was happening in the part I didn't have to fill in. I have a reasonably strong mathematical background and barely no coding knowledge (a bit of Matlab and beginner python training). The whole deeplearning program was going relatively well up to the coding exercises in this course which jump a step too much for me.
Not as great as the previous three courses. The exercises here are much more challenging than before, but not always for the right reasons. A thorough primer on Tensorflow should be made mandatory in this course. A lot of the time you eventually manage to complete the exercises without really knowing what you are doing. The subject matter in this course is also more complex than in previous courses, so more attention needs to be put on really making students understand the fundamentals thorougly. Also, sometimes buggy or inexplicable grader output. Andrew Ng is still a great instructor though.
by Mike L•
I have been a big fan of the series. I think it is a must-take series. I took this course when it was freshly released. The materials and programming assignments were quite good from week 1 to 3. However, the week 4 programming assignment was not ready. I encountered a few issues in the autograder and test data loading. I burned some time tracking down them. Fortunately my fellow classmates were very helpful in the forum. I am sure all problems would be solved in coming weeks. Just keep a mental notes.
Having said that, the materials is worth the pain. Go take it!
by Jonathan S Y P•
La verdad este curso no me gustó mucho porque fue demasiado teórico y habían partes que uno se perdía de tantas formulas... por ejemplo en la tercera semana había una parte de la formula que decía 3x3x8 y como a los 6 vídeos siguientes, explicaron a que correspondía el valor de 8 (Si se hubiera explicado eso desde el primer vídeo hubiera sido más claro todo desde el principio). Este tipo de temas me parece que es más interesante verlo como un tutorial; donde a medida que se va explicando teoría, se va mostrando como hacerlo en x lenguaje, ya sea, python, c# u otro.
by Foad O•
The course is pretty good overall. However, the programming assignments need much improvement. I realize that teaching Python syntax and programming is not really part of this course, but if students are expected to do coding, there needs to be some more detailed lessons/sections to cover the basics. While providing vague, inconsistent and riddle-like "hints" in the middle of the programming exercises make for some interesting brain exercises, they are certainly not helpful at teaching the students what they need to know in order to write correct code.
by Rahul G•
Wonderful course by Dr. Andrew Ng but it would be even better if the course offered EXECUTION EXERCISES following Google AI courses (see below)
Since many of us want to learn the course material and EXECUTE COMMERCIAL (or SEMI COMMERCIAL CUSTOMIZED) CODES and NOT INTERESTED in PROGRAMMING/CODING please provide GUI driven online execution modules INSTEAD OF PROGRAMMING EXERCISES !
Rahul Gupta email@example.com
by Amod J•
Really liked the course content but the true learning was in the homeworks that had the implementation details. After completing the course I was unable to download my own completed assignments as the course assignments were locked out for me. I don't want to re-submit any of them but I want to download my work to be able to refer to it and learn from it. I can see posts in the forum asking me to download them when the next session of the course becomes available, but I cannot afford to keep on paying ~ $50 subscription until it does.
by Mark P•
The content covered is excellent as with the other courses.
However the material in this videos etc have many editing glitches. In addition some of the notebook based programming assignments are misleading and have minor errors that caused auto-grader issues.
In addition the programming assignments seem to be dumbing down. You spend lots of timing solving syntactic nuances of tensorflow, Keras etc rather than being asked to solve cerebral problems that help understanding of the concepts.
by Grant G•
This covers hugely important information and really deserves five stars, but it is fundamentally clumsy. Even leaving aside the unprofessional disaster that is the week 4 assignment 2 grader, the difficulty level is all over the place and the description of the style transfer is borderline incomprehensible (possibly because Prof. Ng is trying to soft-pedal the linear algebra?)
Coursera, Prof. Ng, please take a second look at this one. It needs -- and deserves! -- better work.
by Jalaz K•
Assignments really need to be improved. Of all the courses in this specialization, this particular course frustrated me a bit. Thanks to the discussion groups, I was able to sail through.
Moreover, Grader should provide the summary of error in our submission rather than just showing wrong submission. Course Material was really good. 5 on 5 for that part, but the assignments really troubled me and others as well, as can be easily seen in the discussion groups.
by Andreas B O•
Lectures were great. The descriptions for all applied operations, algorithms, etc. by Andrew are excellent. However, the Programming Assignments this time around demanded a lot of looking up TensorFlow and Keras functions (even during the Keras Tutorial). Especially Week 3 was a struggle for me. At some point, the framework simplicity is turned into rather harsh complexity. A better explanation of what TensorFlow/Keras commands to would be of advantage.
by Asif I•
First of all, thank you for providing such a rich content.
I know its hard to strike a balance between covering content and "actually" delivering them to the student. Course #3 and especially #4 felt very rushed when it came to the exercises. The tensorflow concepts that came back out of nowhere and solutions would have been nearly impossible without the copious hints.
PS: Course 4 "happy house" face recognition assignment was choke full of bugs.
by Nitin S•
Very good introduction to concepts on Convolution Networks. It would have been great to put more emphasis on how actual models like "FRmodel" are trained vs tested. E.g it would be great to provide information on the fact that 3 parallel networks need to be used that share weights. So more exposure to practical aspects of implementation would be useful. Essentially a lot more time can be spent on exercises than what is meant for them
by Vahid J•
Unlike other courses in this specialty, this course was primarily focused on describing some specific methods/approaches (which happened to be very popular) rather than describing high-level concepts. At some points, I had a feeling that the course material reads more like a journal club. While journal clubs can be very useful, I preferred more if this course was mostly focused on overall/generic concepts.
by Michele T•
This was an interesting course. It provides a high level look at face recognition/verification and various state-of-the-art aspects of convolutional neural networks. The one thing I found frustrating in this course was the grader. It was very particular for at least one homework assignment on the order in which you entered your variables. I spent way too much time on debugging for simple things like that.
by Matthew C•
The content was great, and is probably the best available. However, the grader was so flaky it really shook my confidence in the material. I'm the type of person who will try and try until I'm literally about to give up before I look for help in the forums, so I lost a LOT of time on these exercises. This was by far the WORST of the five courses in the specialization. Sorry to yell, but YOU CAN DO BETTER!
by Samuel R•
The Keras and TensorFlow versions used in this course are by now to a large degree outdated. The Newest TF version is at the date of writing 2.3, while the course uses <2.0, so many of the functions used are deprecated in the newer versions
However, Andrew's explanations are great as always except for the convolutional implementation of sliding windows in the 3rd Week. (therefore only 3 stars this time)
by Alan S•
Depplearning.AI: Please do not release content unless it is ready. The content is fine, but the assignments were clearly hastily put together and had basic bugs discussed all over in the forums. In particular, week 4 is a complete mess. Boiler-plate code that doesn't even relate student-content (to load a dataset) doesn't even run for many people. This wastes everyone's time. Really disappointing.
by Bjorn E•
Overall a great intro to CNNs. But the last part of the course on object detection and facial recognition is very superficial. It explains the logistics of the disciplines (how to keep track of bounding boxes, etc), but it doesn't teach how to actually build such a system. The exercises make you fill in a bunch of indices and do vector math, but deliver the actual hard parts inside black boxes.
by Johannes B•
Very good covarage of the algorithms when it comes to analyzing pictures, and a good intro to the theory behind the models. But it is too little emphasis on other uses of convolutional networks like 1d convolutions, causal convolutions and similar. Maybe there are some coverage of these topics in the sequence course in the series, but it should be covered here to a larger extent either way.
by Emanuel D•
All video content of this course where great, but i can't say it about programing assignments. YOLO and Neural Style transfer are by my opion advanced topics. I would more appreciate longer programming excersice, not only something where i only add some piece of code and i hardly understand what is going about. For example, convnets were clear, i could implement it by myself, but yolo no.
by Santosh N•
Course lectures and questions are very good. The programming assignments are also good questions wise, but the grading mechanism is quite annoying. We had to find out clumsy workarounds to get the correct grading, in one case, the code change needed for getting the correct grade did not result in the expected output. Coursera needs to change the method of grading programming assignments.
by Sinan S•
In this course the material/resources regarding Keras is highly insufficient. So far I have made little progress in the programming assignments related to Keras and I achieved this by figuring out stuff by googling which took a long time. If the instructors could extend the course with stuff related to Keras it would be great. At least there should be references to sufficient resources.