2019년 12월 11일
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.
2020년 9월 3일
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.
교육 기관: Prakhar P•
2021년 7월 12일
This is one of the best courses to understand CNN and to have a strong grasp on the fundamentals of Computer Vision and various architectures. I am really happy to have enrolled for the Deep Learning Certificate course. I recommend this to anyone interested in diving deep into neural networks.
교육 기관: Andrei N•
2019년 9월 21일
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•
2018년 3월 10일
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•
2017년 12월 16일
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•
2017년 11월 7일
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•
2020년 6월 15일
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: Raúl A d Á•
2020년 5월 17일
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•
2019년 1월 7일
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•
2018년 11월 4일
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.
교육 기관: Wei F•
2017년 12월 17일
Really enjoyed learning this course. I'm a PhD Student in CS but neither in computer vision or NLP. I feel like these courses are sort of jump-starter, if you would like to learn more about DL and to be expert, there's a long way to go. However, this is really a good starter!! Thanks Andrew!
교육 기관: Sawyer S•
2020년 7월 15일
I think this course offers enough technical details for me to understand how Conv Nets works. However, I find it much easier to undertand the contents if you take the Practice in TensorFlow first, where there is a more practical focus, and understand the big picture. Overall, great course!!
교육 기관: Adarsh K•
2020년 2월 4일
The best place to start Computer Vision! You'll get to implement state of the art Techniques in CV, most with practical Application. The quizzes are very well designed and test your concepts. You'll learn to use open source implementations and build on top of that as well. Wonderful Course!
교육 기관: Dipo D•
2020년 1월 11일
Like the other courses in the DeepLearing.ai certification, this course was also very crystal clear in teaching the concepts. Now, I can confidently read additional materials on Computer Vision. The assignments were also well thought out, kudos to all the TAs. Thanks for the awesome course.
교육 기관: Rahuldeb D•
2018년 9월 4일
Another exceptional course offered by Coursera. There are lot of new concepts to learn in this course.
Prof. Andrew Ng has explained each and every concepts in very lucid manner. I want to give a big thanks to Andrew Ng and all other teaching associates for offering such a beautiful course.
교육 기관: Brandon W•
2017년 11월 24일
Students had some technical issues throughout this course, with the autograder not correctly grading the assignments despite having all expected outputs correct. In time, I hope these issues can be fixed. However, given the level of instruction and quality of the course, still deserves a 5.
교육 기관: Anoop P P•
2020년 6월 10일
The course has balanced of theoretical and practical aspects of Convolution neural network. Moreover, practical sessions encouraged to create a CNN from scratch, use a pre-trained model to fulfil the task. The assignments has helped to practice hands-on using tensorflow and Keras platform.
교육 기관: Ajay S•
2019년 8월 30일
really a great course for the image learning . i love this course well . and thanks for providing me the financial aid for the course . this will really help me to complete my research work on time .
Thnaks. for the profession Andrew Ng . for the designing and teaching a wounderful course.
교육 기관: Soumadiptya C•
2020년 9월 15일
As with all other courses in the specialization "Excellent". Frankly not much needs to be said about Andrew NG's lectures. The only problem I faced was in understanding the Neural Style transfer Topic but doing the programming exercise helped understand the theory behind even that Topic.
교육 기관: Shubhang A•
2020년 8월 28일
Amazing Course, now I have pretty good idea of image processing and convolutional networks. Fun part in this course was definitely last week where i got the basic idea of how to implement face verification and face recognition as well a good idea of Neuro style transfer learning algorithm
교육 기관: HE Y•
2020년 6월 24일
I think this course offers an excellent illustration of convolutional neural network for beginners, even for those who have a basic knowledge about the neural network. The two applications of CNN are quite interesting and useful. I have learned a lot through this course and thanks Andrew!
교육 기관: RUDRA P D•
2020년 6월 20일
Amzaing course on ConvNets but in my perspective anyone who wants to opt this course must have basic understanding how Tensorflow works and basic operations in it. Except every concept are well explained and also research papers are given (for who wants to dive deeper) in the assignments.
교육 기관: Sean C•
2018년 2월 20일
Andrew Ng's explanation of Inception Networks greatly helped to demystify more complex-looking architecture diagrams in Google's Inception Net. This course helped a lot in being to be able to understand the base building blocks, as well as their arrangement & purposes within the network.
교육 기관: Vincenzo M•
2017년 11월 26일
Another super course from Andrew Ng and his team. As the other courses of the specialization, it presents the core concepts clearly. The exercise are foundamental to retain the concepts. As a suggestions, I would substitute the style transfer with an example more useful for real problems.
교육 기관: Nobumasa•
2021년 5월 29일
This course gives me a basics of applications of deep neural networks in the field of computer vison, including face recognition, object detection, style transfer . Furthermore, Andrew provides insightful intuition for convolutional neural networks, which can be applied to other fields.
교육 기관: Niklas T•
2020년 8월 2일
Great course, I learned so much about ConvNets.
Thank you to Andrew Ng and his team.
I loved that they were referring to so many scientific papers. Like this you really get the chance to read them yourself and immerse yourself in up-to-date scientific research in the deep learning area.