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.
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
par 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.
par 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.
par 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.
par 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
par 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.
par 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.
par 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.
par 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.
par 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.
par 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.
par 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.
par Wooshik K•
Feb 11, 2020
Thank you for the lecture contents and programming problems. I am quite sure that I have acquired much knowledge and it will be very helpful to solve my own problems. Also, it would be much more helpful if there are some comments on how to build filter coefficients or filter banks.
par 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.
par 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.
par 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.
par Ravi P B•
Apr 17, 2020
A very detailed and pleasing insight into the amazing world of Convolutional Neural Networks and as always Andrew Sir has been absolutely brilliant in the lectures.This course presents an in depth knowledge of the challenges and various technologies in the field of computer vision.
par Xiaolong L•
Feb 05, 2020
Excellent course! The programing exercises are both realistic and let you build (toy version) of state of art CV system. Many reference to heavy weight papers in the domain in the course, which student who really want to get into DL and CV can read and further expand their horizon.
par 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.
par 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.
Apr 17, 2020
This is a very detailed introduction to ConvNet with descriptions of some modern ConvNet architect. Though I feel that if the programming assignment could be much better if we can implement some of these algorithms from scratch with efficient implementation (using Google Colab?).
par Hasaan A•
Jul 30, 2020
Learned some really exciting stuff. It was great to learn a lot of the classical networks like resnet etc. Although, I wish the programming exercises did not have most of the stuff already filled in (though I understand it is done to make it easy for beginners to complete them).
par Dave J•
Apr 06, 2020
The material is clearly explained by Andrew Ng in his calm yet enthusiastic style. Programming exercises are well structured and explained: if anything I find there's too much hand-holding but having got the basics, there's nothing to stop you experimenting further on your own.
par 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.
par 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.