Jul 11, 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
Sep 1, 2019
This is very intensive and wonderful course on CNN. No other course in the MOOC world can be compared to this course's capability of simplifying complex concepts and visualizing them to get intuition.
By Arvind S•
Aug 21, 2022
This course has an excellent coverage of CNN fundamentals with relevant and very interesting hands on labs in Python and Tensorflow 2. Just finished the Deep Learning Specilization and would like to thank Andrew and the deeplearning.ai team for an amazing experience. Highly recommended !
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.
May 29, 2021
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.
By Niklas T•
Aug 2, 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 CH 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.
By 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
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 6, 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 Chee H 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 Evandro R•
Dec 15, 2020
Another great course by DeepLearningAI and professor Andrew Ng. Convolutional Neural Networks are an amazing part this great field of Deep Leaning that is Computer Vision. Professor Andrew Ng it's simple amazing at teaching those concepts, it almost feels like magic! Wonderful course!
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 Frank 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 Malek B•
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.
By 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.
By Sathiraju E•
Aug 5, 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.
By 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.
By 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.
By 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.