Jul 12, 2020
I really enjoyed this course, it would be awesome to see al least one training example using GPU (maybe in Google Colab since not everyone owns one) so we could train the deepest networks from scratch
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.
by Wei F•
Dec 17, 2017
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!
by Sawyer S•
Jul 15, 2020
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!!
by Adarsh K•
Feb 04, 2020
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!
by 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.
by 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.
by 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.
by Anoop P P•
Jun 10, 2020
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.
by 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.
by Soumadiptya C•
Sep 15, 2020
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.
by Shubhang A•
Aug 28, 2020
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
by HE Y•
Jun 24, 2020
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!
by RUDRA P D•
Jun 20, 2020
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.
by 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.
by 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.
by Niklas T•
Aug 02, 2020
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.
by Chun-Huang L•
Mar 22, 2020
This course teaches CNN from the very beginning to the most details. Its examples and assignments are very impressive for people to know what happen in the model and how it works for many different applications. I can realize most CNN-related research papers after finishing this course.
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
by Rahul S•
Apr 30, 2020
This course gives you adequate foundation to build upon your knowledge in the subject. The structuring of course is perfect and assignments help to pick up difficult codes so easily. Andrew is an exceptional teacher who knows the field and shares his experience and knowledge so humbly.
by 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.
by 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.
by 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.
by AKSHAY K C•
Mar 19, 2020
The course had a very clear outline starting from the basic fundamentals of CNN and progressing steadily towards the applications ranging from facial recognition to neural style transfer in the final week. Kudos to the instructor and his team for delivering such an outstanding course.
by 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.
by 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.
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.