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: Dipo D•
Jan 11, 2020
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•
Sep 04, 2018
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•
Nov 24, 2017
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: Ajay S•
Aug 30, 2019
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: Sean C•
Feb 20, 2018
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•
Nov 26, 2017
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: MOHD F•
Jul 23, 2019
Convolutional Neural Networks by Andrew Ng is a Great course to start into the of CNN's Terminology for DeepLearning. This course provides me with a solid background in how the Convolutional Neural Networks works internally. Great lectures ........... Great everything thankyou Coursera
교육 기관: Miroslav M•
Apr 24, 2019
I've gained very important knowledge for Image verification and recognition algorithms using ConvNet models. These models are used nowadays powering robots and self-driving cars. Thank you very much deeplearning.ai for this opportunity to get closer to finishing my new carrier journey.
교육 기관: Janzaib M•
May 06, 2018
Very very well designed homework. Gave me a really close feel of deep learning for computer vision. The great thing is, in this course you play with very very state of the ConvNet architechture. Thank you so much Professor Andrew NG and your team. A very big contribution you have done.
교육 기관: Huang C H•
Nov 24, 2017
Convolutional Neural Network are exciting to learn, but its concept can be quite abstract. However the materials are delivered progressively, and in a concise manner. The programming exercises are challenging. I hope there was more in-depth introduction to Tensorflow and Keras, though.
교육 기관: Feng W•
Mar 15, 2019
I have some problem doing week four programming assignment "Happy House Face Verification/Recognition". The pre-trained model "FRmodel" wouldn't be loaded (waiting for over half hour). I still managed to submit the assignment and passed the test without running out the correct result.
교육 기관: badreddine m•
Dec 24, 2017
it is my second courses in coursera after Machine learning by Andrew Ng and Stanford university, I'm very satisfied by the courses quality and encourage you to go further, I'm a follower of coursera courses and one day I will contribute to share more knowledge using coursera platform.
교육 기관: Sami•
Feb 15, 2018
i think that's the most important course for me, of course all of them, where very very useful, but being an undergraduate Robotics engineer, the most essential thing is to learn image processing and how to make your robot think and learn and detect object and learn from environment.
교육 기관: Sathiraju E•
Aug 05, 2019
Amazing course. A lot of knowledge packaged into one package. This has been the most useful course in the deeplearning.ai. Thank you Andrew and team. Lot's of interesting stuff and knowledge has been shared out here. Only the back propagation for CNN was missing but otherwise great.
교육 기관: Yernur N•
Jul 18, 2019
It is an essential course for those who wants to boost their general knowledge in the area of CNNs. It will give you a great foundation to build on your career and further learning. I struggled a bit with Keras, but I am planning on taking another course to learn this field further.
교육 기관: Matheesha A•
Jun 21, 2019
This is an excellent course to learn the concepts of Convolutional Neural Nets. The hands on experience by the weekly assignments were very helpful to understand the concepts. I strongly recommend this course for the students who are interested in learning CNNs. Thanks Prof. Andrew.
교육 기관: MADAN M•
Feb 22, 2018
I got thrilled by the lectures and its assignments. One thing that I would request is a lecture on how to use pre-computed models, in all the assignments we are using pre-computed models. Andrew explains why we should use them but in practice its seems little difficult to use them.
교육 기관: Shaelander C•
Dec 10, 2019
Very informative course . Professor Andrew Ng has done a great job of explaining most of the concepts of CNN. And Assignments are really good to apply what we learn in the lectures. Professor Andrew is the best professor I ever came across the style of his teaching is unmatchable.
교육 기관: Animesh S•
May 21, 2019
Great course, concisely conveys both techniques and advice for practical implementation of Neural Networks in Image recognition. Great for a person who is already familiar with the idea of Deep Learning and want to take it forward, and ties in perfectly with the specialisation.
교육 기관: Ali S•
Aug 10, 2018
This course is a perfect way to teach these high-level concepts. They made it easy, step by step, and practical. You can learn not only convolutional neural networks in both conceptual and practical way, but also a lot of tips and tricks about tensorflow, Keras and even python.
교육 기관: Shivdas P•
Jan 01, 2020
The course is well structured, especially the exercise where one has to code the complete CNN example. It gives good insights on how to use the frameworks such as TensorFlow and Keras. Feel comfortable in understanding the concepts around CNN and it's implementation using TF.
교육 기관: Gurubux G•
Aug 20, 2019
One of the toughest and most exciting Course I have completed on the internet. Thanks a ton Andrew! I wish I can work with Deeplearning Team someday, so that I can learn every week, every day and probably explore the deepest of the Learning ocean potential that the team holds
교육 기관: Gilad R•
Aug 13, 2019
I really liked the dive into academic literature combined with the wide view of CNNs across various applications. The programming exercises were very revealing and informative, although a little more guidance on TensorFlow technicalities would have helped accelerate learning.
교육 기관: Bernard O•
Oct 31, 2018
This is quite a challenging course. Critical lessons on convolutions are the biggest value to me on this segment of the course. Takes a lot of the mystery out of CNN, but need to work hard at it. A very rewarding experience but does come with a few tear-my-hair-out incidents.
교육 기관: Stephen V K•
May 17, 2019
The course does an very good job of explaining the concepts behind different types of neural networks, but the homework assignments pretty much only test these concepts. Students should not expect to gain any significant experience coding neural networks in keras/tensorflow.