Jan 13, 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.
Sep 02, 2019
This is very intensive and wonderful course on CNN. No other course in the MOOC world can be compared to this course's capability of simplifying complex concepts and visualizing them to get intuition.
교육 기관: Yilun Y•
Apr 06, 2019
Overall an awesome course, however, it somewhat lacks some important topics and models such as SSD, Faster RCNN, mask RCNN, etc which are even more frequently mentioned in literature and applied in real world projects. This course really sparked my curiosity and passion in deep learning, I actually learned the models mentioned before by reading the original paper and many useful blogs. This is a long but rewarding journey, I would also like to see more topics be covered in this course and let more people know how these state-of-art models work and how they really change the world.
교육 기관: Xiang J•
Nov 04, 2019
I really like this course, because it not only taught me the exciting new topics that I always want to learn, such as object detection algorithm and neural style transfer, but also it gave a solid introduction to the concepts of convolution. The assignments are great, it is fun to do and it also helped me more concretely understand the materials of main course. As to further improve the course, may be it would be nice to build a whole end-to-end pipeline including training the main convolution model in car detection as I know in Google colab even public users have access to GPUs.
교육 기관: Mukund C•
Oct 15, 2019
Loved it!! Loved it!! Loved it!! I wish there was a little bit more engagement from mentor side as well as updates on the coursework with the latest developments in the object detection field. I also wish that there were a little bit more involved programming exercises, maybe one in "training" where one has to label objects and "train" a neural net. One of the things that I missed in the course is an explanation of the Neural Network architectures and why they work - e.g. the VCCG-16 or Inception Network - for example. Maybe one has to read the papers to understand them?
교육 기관: Shankar G•
Jul 08, 2018
This part of the CNNs course in DL was awesome and long enough. It started with foundations of CNNs, where the concepts of CNNs layers was made very clear. Programming assignments helped understanding the layering activation properly. The good part was DeepCNNs case studies explanation with its pros and cons, plus the practical advice for using ConvNets. Also this course provided few papers applications like object detection, face recognition and neural style transfer which was amazing. All the quizzes and programming assignments refreshed the concepts in a good manner.
교육 기관: Abhilash V•
Apr 19, 2018
This course covers the basics of convolutional neural networks , resnets, inception nets, yolo, style transfer, face recognition.The programming assignments mostly for yolo and face recognition is done with transfer learning , i think its only fair as they are computationally expensive to train.I am confident about all the materials covered in this course Andrew Ng as always breaks down the problem to the basics so you can understand them.Its a great course if you want to know and implement the well known computer vision problems with the well known algorithms.
교육 기관: Alouini M Y•
Dec 26, 2017
This course helped me consolidate my computer vision knowledge. In fact, I had some prior experience but felt left behind given the current rapid advancements in the field of computer vision (thanks to deep learning mostly). The material is up-to-date and the assignments (especially the notebooks) are very pleasant. I have learned a lot of modern CV techniques: YOLO for image detection and localisation, style transfer, face verificiation with DeepFace, and many more. I recommend to anyone that is serious (or at least curious) about modern CV techniques.
교육 기관: Jeffrey S•
Apr 10, 2018
I had a tough time on the programming exercises - mostly due to poor Python/Numpy/Tensorflow experience. I did find the material really interesting. The teaching style is great - much better than other courses on AI I've started. Andrew is terrific and pleasant to learn from. While totally different from the megastar CS50 (EdX) approach, he manages to make a complicated subject understandable. I have my list of subjects I need to go back and review, but I really feel like I've gotten a good perspective on the Deep Learning field from these courses.
교육 기관: Martin K•
Jan 15, 2019
Andrews unique way of presenting complex theoretical concepts in a compelling and easy to understand manner was essential for my learning success. Attending this course was fun. Even though the programming assignments were pretty tough in this course (for me the toughest of all the courses in the deep learning specialization), I managed to complete this course in (my) record time. This might be mostly due to the understanding of the underlying mathematical concepts which were outstandingly well presented.
Totally recommend this course!
교육 기관: David A G•
Mar 16, 2018
The course was excellent. I really enjoy Andrew Ng's courses: complex stuff made easy and lots of practical applications.
The only thing that I would try to improve is the time the staff dedicates to check the forum to solve student's questions. I personally got stuck at one of the quizzes and it was hard to find any clue that might help to understand the right answer. Also, some really interesting general questions on the forum were not replied by anyone. I'm sure some expert help on the forum would bring great value to the course.
교육 기관: Marcel M•
Jun 30, 2018
For an engineering discipline, there is nothing better than employing the latest state-of-the-art techniques in solving real-life problems. That's the inherent value of this course the fact that you learn how Deep Learning is having an impact on so, so.. many, diverse areas such as Self-Driving Cars, Object Detection, Localization, Classification, Verification, Recognition and much, more. I highly recommend this course to anyone who wants to be an adept Deep Learning Practitioner. Kudos! Team DeepLearning AI. Keep up the good work!
교육 기관: J K•
Feb 25, 2018
The best course (yet). A good balance between theory and practice, although the complete lack of TensorFlow and Keras fundamentals can be a bit frustrating. Additionally, the use of numpy operations (add, multiply and such) gave the impression that you'd correctly done a function assignment (the check values were OK), however, the grader failed to accept it as being correct (which was justified), however, an indication that it was incorrect (or some comments in the accompanying text) would've saved me 30 minutes of searching.
교육 기관: Ahmed E S A H•
Nov 13, 2017
This course is very good. But i hope, after the course's weeks end, to add one more section to explain the recent publications and the most important challenges in the course field. In my opinion, this section will help the researcher to find a path to start research in course topic and try to find a new contributions that can help them specially if there are new master's or PhD students, they can figure out quickly where to start there research topics.
Thank you for your great effort and i hope i can learn more via Coursera.
교육 기관: Asif M•
Dec 04, 2017
Its a very complicated topic and Andrew Ng and his team have made it very easy for us to learn the core concepts and easily do the programming exercises. Needless to say, we need to spend some additional time outside the course if you want to get a deeper understanding of the topic as well as learn more about the nuances of pre-processing and loading data/models abstracted away by the utilities as well as the detailed instructions in the exercises.
PS: The discussion forum is super helpful, especially when you need some help.
교육 기관: Virginia•
Feb 24, 2019
The course is a perfect balance between theoretical explanations, application in programming and tips that can be helpful if you intend to work with CNN. I had not seen CNN before, and I didn't feel lost at any moment. Every doubt I had was perfectly answered in the forum. You don't need much of an experience with TensorFlow or Keras to do the labs, which are accompanied by thorough explanations of what is required; on the other hand, there are "extra" tasks for people who want to go more into depth in each lab.
교육 기관: Vincent F•
Feb 08, 2018
Overall a very good course for the instruction. Found only two omissions with the programming assignment notebooks. One was where a function expected a tensor but the parameter we were encouraged to provide was an array. Had to use a convert to tensor call. The other was a mismatch between the expected output block and the grader. This has been noted already but has not yet been fixed. But quite minor all in all.
Really liked the links to the academic papers that are the source of the models used. Thanks again.
교육 기관: Maximiliano B•
Jan 02, 2020
In this module of the specialization, you will be familiar with several types of convolutional neural networks and how do they work in details. Compared to the previous modules, this one requires more time due to the complexity of the subject as well as the programming assignments that are more difficult. After this course you feel comfortable to read all the papers covered as references throughout the course . Moreover, Professor Andrew NG explains the content clearly and it is a pleasure to watch his videos.
교육 기관: Jose M L•
Dec 07, 2017
Needs a few corrections on the last week's assignment. Other than that great course. I recommend people to go deeper (no pun intended) in learning Tensorflow and Keras by self studying via other resources (books, videos, tutorials) since the programming material is too extensive to teach in a course like this which seems intended to master the basic concepts and the most important results in convnets. Thank to Andrew and the TAs for an excellent course. See you all in the Sequence Models and last course!
교육 기관: Kai-Peter M•
Oct 28, 2019
Great course!!! The best online course I have ever taken! I enjoyed almost every day I participated in that course, really an educational treasure! It is so comprehensive and detailed at the same time. Due to the good presentation of the topics it was really understandable. The only thing I would wish for future participants: please make it easier to get the complete Jupyter notebook environments from the Coursera platform once completed. I spent a lot of time here - even after consuming the related blogs.
교육 기관: Matthew B•
Jan 01, 2018
Great course. Brilliant overview of CNN with recent implementations. I understand the limitations of covering only so much material in 4 weeks. Wish the course could have gone deeper on training YOLO. I had to do this myself from the darknet website with some other tutorials. Something to consider, implementations of Unet and Mask RCNN may be even more useful for precise object masking/detection rather than bounding box in the future. May be worth mentioning these techniques as they develop further.
교육 기관: Shehryar M K K•
Apr 30, 2018
This is the 4th course in the series of deep learning that I finished. It was very enjoyable. The topic is deep and the instructor referred to papers and their implementation as exercises. Inception networks and ResNet exercises were my favorite and I learned a lot from them. The other assignments were good but weren't enjoyable as the two mentioned above. I would suggest the instructor incorporates some reading materials in the course which can be tested in the quizzes. Thank you for making this course.
교육 기관: Aniruddha S H•
Mar 31, 2019
Excellent course. This covers almost everything you need to know about computer vision. starting with how Kernels detect edges, how convolutions work all the way to Object detection, face recognition, style transfer. This also includes references to some important deep learning papers which you must read. Programming assignments really help to understand the concept. but, some assignments are not clear and dimensions are confusing. Successful submission is a relief :P. Overall an Exceptional experience.
교육 기관: Waleed A•
Dec 01, 2017
As someone who is studying AI and Neural Networks for the first time, I can say that this course was a very enjoyable experience for me. The structure of the information content makes the learning experience all the more valuable, and makes the learner yearn for more. Compared to the previous 3 courses, this course gives a little more mobility in terms of thought process and problem solving by introducing Keras, and allowing the learner to play around with models. All in all, it was well worth the time!
교육 기관: Brian L•
Nov 01, 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.
교육 기관: David 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.
교육 기관: Luis E R•
Apr 03, 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,