Very informational course and the pace is reasonably challenging. Knowledge learned in this course are super practical and can be directly applicable in many areas. The Keras curriculum is especially welcoming. Do take note that course 2 and TensorFlow are sort of hard requirements to be successful in this course. I did course 1 and this course together, and it turned to be not the best choice.

Also, the risk of taking a new course is there were definitely several technical glitches which resulted in more troubleshooting than necessary. I believe this would be better with iterations.

Good introductory course for ConvNet and its trending applications such as object detection and facial recognition. Materials are presented to give students more of an intuition and process to carry out ConvNet applications rather than a rigorous mathematical understanding. Basic TensorFlow knowledge is highly recommended or one may face difficulties or confusion during assignments. I personally would like the course materials to have more depth, so to really nail in every step in building a ConvNet application (since as of now the content can be surface-deep and easy to forget).

The course instructor did a good job in presenting the basics of CNN and some of its applications in the domain of computer vision. The applications presented are 1) Image classification 2) Object detection and 3) Neural style transfer. As a researcher myself, what I most appreciate is that the instructor presented the topics to sufficient depths enabling the reader to appreciate the underlying theory and at the same time keeping it high-level. For those who would like to go more in depth, the relevant citations are presented in the lectures. Overall a very satisfying course.

Great content! Andrew really makes the concepts crystal clear. The lectures are very coherent and extremely organized, in terms of the actual contents. I've took MOOC in CNN before, and personally felt that Andrew's version is the best.

I took one star off for two reasons: 1) Sometimes Andrew would say half of the sentence and then start over -- it seems to me that those were to be taken off during editing of the video, but they are not -- hopefully they'd get fixed at some point; 2) It would be nice to upgrade the code to TF2, as TF1 has become less popular for a reason.

Andrew is great as always, but the assignments are no so great. It feels like you are doing a little piece of useless code to the full application. The order of the functions and the extensive use of Keras/TensorFlow is frustrating and very confuse. Maybe, in the future, you can add some explanations about these tools. Also, given the assignments the weeks is no longer 4/4.5h, it can take hours just to understand some silly syntatical tf related errors. But I now know how to get started with my project and where to look for hints and improvements thanks to you guys.

The content is really great. It is giving very good overview on the state of the art, and how convolutional neural networks can be useful. I think it is hard to get such a great overview in current deep learning books that usually focus on more theoretical aspects, which are covered in this course. The only negative point I would say is that is it not always easy to understand how to use some very specific python tools, and one can easily get stuck into implementing a single line of code. However, the discussion forum provides great resources to solve these issues.

Excellent content but platform was frustrating at times. The exercises still use TensorFlow 1.2.1, which created some aggravation because newer versions have rearranged many functions into tf.math.xxx, etc., so the documentation "hints" give answers that don't match what is needed for the exercises. Also, the grader is maddeningly finicky, giving no points for code that is in fact correct. It's like playing "Simon Says". It seems that a decent chunk of the required effort consists of scouring through the discussion forums for workarounds to these glitches.

This course gives a comprehensive introduction to CNN. However, I am not satisfied with the exercises designed for this course. In each assignment, you are only required to fill in a few blank spaces, leaving a lot of important parts as black boxes. Please make the assignments more like complete projects. I know it will take people much more time to complete and may turn some people away. But it is crucial to give a complete picture of each programming exercise assigned. Anyway, I still want to thank Andrew and your team for offering this series of courses.

The material was very interesting and the technologies introduced were very good. The only problem is that unlike the previous 3 courses, this one seems to have been done in a rush.

The video aren't edited that well. (there's weird sentence repetitions, and the coding parts are sometimes bugged up, very very hard to get to the right answer, and had a few typos. Hopefully those little bugs will go away, but overall it was an informative course. Some of the topics were so interesting, I felt like they would entertain an audience uninterested in deep learning,

Positives:

1) Well designed course that takes you through the concepts of CNNs step by step and introduces cutting edge state-of-art applications based on it.

2) As always well prepared lectures effectively deliver the course material.

Negatives:

1) Course lectures should have covered overviews of actual models used in assignments (YOLO for object detection, Inception network for face recognition..) and the actual cost functions that were used to train them. That would have helped a lot in getting more practical real life feel helping user community a lot.

The content is great. The best so far in the DL specialization perhaps but I lost a lot of time in the last week's assignment where the grader was prompting wrong output in spite having written a program that gave the correct output. That was very frustrating and the worst part is I still don't understand why that was happening(I got full score after submitting the same program for the 10th time) and even jupyter notebook took ages to load(my net speed is 140MBPS). Apart from this, it was a really nice course and the experience was very satisfying.

Like the others, a fantastic course. Some of the videos and exercises seem a little underprepared, and require more time examining the discussion forums than the first three courses. For example the NST tutorial appears to require using np.square rather than tf.square to obtain expected results. This is not documented, and obtaining other results may result in passing, but it is unclear the ConvNet is working as it should. However the course covers current and quite advanced topics extremely clearly, and includes great links to original papers.

The content is superb, but the realisation of the course seems a bit rushed in comparison to the previous courses in the specialisation. The editing of the videos has many issues (fragments that were meant to be cut out are left in the lectures), there are many typos in the notebooks and the references for documentation are outdated. In one case the grader of the notebook has an unexpected mistake built in (it expects one rectangle area to have a negative value and gives a 0/10 grade when you try to prepare the code for such edge case).

Everything was absolutly great and undoutdetly quite usefull , I have one complaint though , if I had a problem in assignments( mostly related to grader output like U-net segmenation which I passed all the tests successfully but somehow my grade turned out 66) and I 've put my problem in discussion no mentor would answer it and if they did it was always the same "The topic has been moved somewhere that I don't know"

anywho it was great thanks for allowing me to take this course , it really helped me I hope I could return your favor

Very good course. I only wish there were even more examples with harder homework. I did every homework in less than an hour and felt like I still couldn't reproduce much on my own after taking a class. The lectures are great and cover most of the material you need. But as programming assignments go, it is still a very introductory class and you are definitely not ready to write much on your own afterwards. But I think they can improve a lot with a few more homework jupyter notebooks that are more advanced that their current ones.

Course provides good overview over state-of-the-art techniques in computer vision. The lectures are mostly clear although week 3 and 4 do lack some explanations on how these systems are trained. E.g. the style transfer lecture should emphasize that optimizer acts on the pixels of the generated input image using the without altering the weights of the network. In terms of the programming exercises, I would prefer to work through the code to better understand the structure, rather than just filling in bits. Still a great course!

Great explanation of advanced topics in deep learning and computer vision. This course deepened my understanding of convolutional neural networks in significant ways. The videos could use a bit more editing to remove repeated phrases, but it didn't distract too much from the learning. The projects are very good at giving hands on experience with the concepts and the tools. It would be great if they could be updated for newer versions of Kera and Tensorflow. Overall, I would recommend this course to anyone interested in CNNs.

Excellent lectures with really engaging explanations and examples. The programming exercises are also really well structured and require real work and understanding where code is required. It is very exciting when you get the models to run and produce a result. One concern: My sense is that we are moving a little fast on the overall process of solving a problem. That is, the programming exercises are so well structured that I am not confident that I could solve the problem without the exercise frameworks.

The course content in the video lectures is very good -- all the visual explanations and Andrew Ng explanation is easily understandable. But as far as assignments are concerned, many of the functions are readily implemented and just called for use. It would have been good if the student is guided in implementation from scratch. Though a top view understanding from theory to implementation is obtained with solving the assignments, it would have been more profound if everything could be implemented from scratch.

The material in the course is very good, specially the notebook exercises.

There are some technical aspects that prevent me from giving it 5 stars.

Namely, the english captions of the videos have too many mistakes, considering how easily they can be fixed.

I don't see the point of having an extra 'Reading'material with corrections for the videos. Why not adjust the video once and for all?

Finally, the number of clippings and strange cuts and jumps in the videos have increased with respect to previous courses.

Comprehensive course with a huge dose of knowledge about different CNN architectures, image recognition and neural style transfer as the last assignment. The lecturer teaches a lot about the theoretical part and programming assignments are demanding. However, after completing the course, I don't feel confident enough in using Tensorflow as there are some exercises that I've finished mostly by trial and error without actually understanding why they worked with so many other questions about theory in my mind.

Overall a very good course. Assignments have errors in the code. This is documented in the discussion centre going back a couple of years, mentors help explain, but mentors cannot edit to fix the code, and the course supervisors have long since disappeared. So you have to submit incorrect code to pass, then fix the code for your personal private code store - as the fixed code generates the correct numerical answers that unfortunately do not match the numerical answers that the grader requires to pass you!

Excellent survey of the area. However, programming exercises vary frustratingly between cut-and-paste and obscure tricks that require burrowing through the forum. Long-term value significantly reduced by the apparently intentional decision to not support efficient download of needed material. Unfortunately, Coursera's notebooks are also not stable, routinely resulting in lost work, followed by multiple tries to log back in. Love the content, but the rest of it seriously reduces productivity of study time.

The course material is very interesting, but also somewhat hard. It takes everything we learned in the previous 2 courses to the next level.

This is a good thing! However, the programming exercises do not really require the student to understand much. In most of the exercises I copy pasted from the examples and used some trial and error. Contrary to the previous courses, I feel that the exercises were something I did to pass the course - and they didn't really help me understand the material better.

Overall, this course clearly explained the basics of CNNs. However, the neural style transfer network could use more details. For example, after calculating the loss function which layers are updated in the network? Is it all of them or only the ones used to find the style image. Also, 'a_G' not being evaluated in the assignment wasn't clearly explained. Lastly, please have a couple of more assignments on tensorflow. I've done the earlier courses but they don't give enough of an understanding.