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.
Dec 12, 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.
교육 기관: Jeffrey T•
Mar 30, 2020
The intuition and examples made this course easy to understand and learn. I loved how Andrew decomposed current published papers into an easy to understand format. All of the important points to remember were highlighted without wasting time on the minutia. Thanks for all the hard work put into the course.
교육 기관: Alexandre M•
Nov 29, 2019
One of the most important courses in the Deep Learning Specialization in my opinion. Good content, enjoyed the homework, lots of details for beginners and extra resources for more advance content. Would definitely recommend for anyone interested in working in Machine Learning especially in Computer Vision.
교육 기관: Avineil J•
Dec 04, 2017
Exceptional Course. Learnt a lot from it. Takes a different approach to teaching than other courses in the sense that more focus is on applications rather than training of models for which a GPU cluster is a must. Thanks Andrew Ng and his team for the wonderful course. Looking forward to sequence models :)
교육 기관: OMAL P B•
Apr 11, 2020
An amazing course to get an advance knowlege and practise "Convolutional Neural Networks". Andrew Sir makes the math and concepts behind the scenes very easy to understand. The course is easy to follow as it gradually moves from the basics to more advanced topics, building gradually.
교육 기관: Jizhou Y•
Mar 08, 2019
Professor Andrew is really knowledgeable. The lecture videos he makes are really helpful for me. I really learn a lot from them. Also, the recommended learning materials such as academic paper he recommend are really useful for me if I want to further my learning on the residual network or YOLO algorithm.
교육 기관: J.-F. R•
Feb 18, 2020
Great course by Prof Ng. I had taken his Machine Learning course a few years ago, so expected high standards of content and assignment preparation - I was not disappointed. Staff is very responsive and helpful in forums as well. I highly recommend it. Taken as part of the DeepLearning specialization.
교육 기관: George Z•
Aug 29, 2019
Exceptional course taking you into the real world of deep learning by exploring new concepts and classical architectures like LeNet-5, AlexNet, VGG-16, ResNet, R-CNN, YOLO, FaceNet and Style Transfer that propelled deep learning in new heights. Loved every part of it (minus some hiccups with the grader).
교육 기관: Mukesh K•
Aug 29, 2019
The course is just awesome both in terms of content that is being taught in the lectures and the assignments. Though, I think the last week is not that much important for the industry purpose but definitely it is good for those who are interested in non-industrial use of tensor flow and neural networks.
교육 기관: Ignacio H M•
Mar 26, 2020
I finally understand YOLO! This course has the best material available on CNNs. Even though I come from a MSc in Computer Vision and Machine Learning, we didn't have enough time to fully cover 'complex' architectures such as YOLO. Thanks to this course I feel more up to date in the Deep Learning field.
교육 기관: Victor F d P•
Apr 08, 2020
Once more Andrew steps up as a brilliant teacher. I'm a biologist looking to improve my data science skills to better tackle medical imaging problems. I'm confident to say Andrew is the reason I'm going to make a difference in low resource communities in the future. Thank you, Andrew, you are awesome.
교육 기관: Scott H•
Feb 05, 2018
I really enjoyed this course. I found it pretty approachable. FWIW, I'd taken Andrew's original ML class, but then skipped 1,2, and 3 of the new one (and jumped into 4) The course really holds your hand, so be prepared to force yourself to try some of this on your own to be sure you've understood it.
교육 기관: Harsh B•
Nov 06, 2017
This course is intended for ML learners who have background knowledge of NNs and want to enhance their scope of knowledge in CNNs. Prof. Andrew has been an amazing instructor. The material used in this course is mostly based on Tensorflow, so make sure to have a bit of prior knowledge in Tensorflow.
교육 기관: Pasit J•
Apr 09, 2020
I have learnt a lot new things in this course, constructing exciting image/object detection projects with Tensorflow, Keras and even plain Numpy. Also, Andrew well explains many complex network architectures which illustrate various perspectives of the applications of convolutional neural networks.
교육 기관: Vidar I•
Feb 13, 2018
This course really gets you started working with CNN. The only downside are the "bugs" in the assignments. My advise is to read the discussion forums before you do the assignment to know if there is a bug that you should know of before submitting.
Beside this minor bug, the course content is 5 star.
교육 기관: Damian C•
Mar 26, 2018
Really enjoyed learning more about the current state of the art of image recognition models. Although the structure needed can be at times overwhelming, the concepts are clear and implementation via open source packages make it feasible. Many thanks for making this available, keep the good work!
교육 기관: Maciej F•
May 08, 2019
Somehow, a bit harder than rest of the courses for me. I had problems with tracking dimensionality and tensorflow notebooks were hard and difficult to debug. I think it would be nice if tensorflow has its own as a course or 2 weeks maybe. But anyway the concepts explanations is great as always!
교육 기관: Juan M E B•
Apr 19, 2018
Excelent course. ConvNets are an eye-opening subject and the course explains the main concepts and applications in a simple way, indicating the source papers to understand better. I'd only ask for a couple of videos explaining in more detail backpropagation and the upload of the missing slides.
교육 기관: Andrei N•
Sep 21, 2019
The content, examples, assignments, and quizzes are thoroughly developed. All the courses of the specialization share the same notation and lead a student from basic concepts to complex ones helping to develop an intuition on each step. The best course on topic of Deep Learning one could find.
교육 기관: benedikt h•
Mar 10, 2018
great ! It is complex though, don't get fooled by the doable exercises - to really understand you can take several loops.
Imagine someone breaks up recent complex research paper into python notebooks for you and you get this delivered like a delicious food - this is how I feel about this class.
교육 기관: Jun W•
Dec 16, 2017
This is an excellent course. Although I've got 100%, there are still some details and intuitions need to be figured out. Maybe I will go over it again. And of cause, I'm looking forward for the fifth course. I wish the fifth is not the last course. We still need to know reinforcement learning.
교육 기관: Dr. R M•
Nov 07, 2017
Very informative lectures with simple explanations of what the algorithms are doing. The programming assignments are extremely detailed and well explained. This makes it very efficient and fun to learn the concepts of Conv Nets, Res Nets, the YOLO algorithm and so on in a short period of time.
교육 기관: Jonathan M•
Jun 15, 2020
A great course overall. Ties together the concepts presented in the first 3 courses and does a great job of showing some very practical real life applications - the programming assignments show a wide range of practical applications of deep learning like face recognition, art generation, etc.
교육 기관: Raul d A•
May 17, 2020
It was a great course. You end up with a pretty good understanding of convnets and their different applications and algorithms. For sure this course set up the basis for image processing work and research, although it is necessary to refresh concepts and go over the notebooks to fix concepts.
교육 기관: Nour A•
Jan 07, 2019
The course explains topics I used to consider as "complicated" in a very clear and simple way. The videos and quizzes about theoretical concepts accompanied with programming assignments and extra reading material give solid understanding of the topic, its current trends, and future direction.
교육 기관: Igor C C•
Nov 05, 2018
I think that should have an optional video with the mathematics behind the convolution/cross relation, showing element-wise operations on a small volume with more than one channel. I know most people will find it boring, but i think it will make easier to fully comprehend the 1x1 convolution.