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.
Sep 02, 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.
автор: Jason J D•
Aug 18, 2019
Another wonderful course in this specialization. The course covers many important topics in the field of Deep Learning such as CNN architecture and models, ResNets, Object Detection, Face Recognition, Neural Style Transfer and even a tutorial on the popular DL library Keras. The programming exercises and fun to complete and the course content is top-notch as always from Prof. Andrew.
автор: Sriram V•
Oct 17, 2019
Programming exercises need to made really with right structure as the YOLO one was very poor. Problems are very easy and makes this course very simple. We need to incorporate right amount of programming along with concepts, make it tough and train us also really well in the ideas. Concepts are absolutely fine, it takes the slow pace to make us understand deeper ideas and intuitions.
автор: Nelson F A•
Aug 23, 2019
Excellent course with many hands on examples and filled with important resources on CNN architectures and other best practices. There are many optional reading material that I'm sure to come back too. The only thing missing was a little more insight on backpropagation on CNNs, although an example of it is given in a coding example. This is a course I will be coming back to for sure!
автор: Ashutosh K•
Nov 22, 2017
The best part about the course is the focus on understanding the basics. It takes time and effort to learn and follow through the lectures but once you understand the basics clearly, everything else becomes so much easy to understand. Not like some of the courses out there which push you into advanced coding from day 1 and then move backwards to basics, this course is so much better
автор: Samuel Y•
Dec 10, 2019
This course was awesome -- albeit pretty hard. I understood most of the concepts when learning them, but it was easy to forget a lot of the implementational details and such. Dr. Ng does such a good job, nevertheless, both presenting the material (which is straight out of cutting-edge papers) and also offering tips for actual implementation. I plan to make an app after this course.
автор: Quentin G•
Aug 09, 2018
Cours très intéressant et d'un niveau bien supérieur aux 3 modules précédents. J'ai vraiment du réfléchir sur de nombreux exercices de programmation pour arriver à mes fins. Merci beaucoup !
Very interesting courses. The difficulty level is very higher than the 3 previous courses. I really had to think everything twice on the programming assignments before submitting. Thanks a lot !
автор: Rex F•
Jan 30, 2018
i can't believe i learned so much, can read complex equations and translate them .. it's like a condensed math specialty mixed with learning real-world utilities and tools .. hey, i know from this course how to quickly and (almost) effortlessly prototype recurrent and other deep networks, how cool is that? because of this course i also became a contributor to Keras! yay for me :)
автор: Roman V•
Feb 24, 2020
I have become a great fun of deeplearning.ai and Andrew Ng. Thanks a lot of great high quality materials. Going through the specialization I'm falling in love with Deep Learning. I believe historically, deep learning, and especially ConvNets related papers are usually pretty hard to comprehend by simply reading them. This course made it so much more simpler, it is unbelievable.
автор: Jamie K•
Dec 23, 2019
Lots of new concepts in this course. I liked the literature review sections and the fact that Andrew starts to show you when it makes sense to pull someone else's model down and use that rather than building something from scratch. The programming exercises were also pretty good - I had to think in a number of places though they are still a little too structured for my liking.
автор: Najeeb K•
Aug 24, 2018
A great course providing in-depth theoretical understanding of Convolutional Neural Networks and state of the art model architectures for various Computer Vision tasks. I have been doing Machine Learning from past one and a half years but the course content still gave me wealth of knowledge in a structured format that I yearned for so long. Thanks Prof Andrew and the team! :)
Jan 14, 2020
best course in world or unvierse to understand the basics and complex details of convolutional neural network .i would give an oscar for this course . I was so woried about the complex diagrams that i saw in internet about CNN but this course made it look very easy i was totally suprised how complex details were explained in simple manner .I would recommend this to everone .
автор: Manjit P•
Dec 07, 2017
This course covers lot more material and it is more application oriented compared to last three courses. I had to spend lot more time and effort for this one. Also, there are some bugs during submission of the assignments. There is enough discussion about those but I hope Coursera takes care of those in the near future. Nevertheless, I always enjoy Prof. Ng's lucid lectures.
автор: Yash M B•
Jan 20, 2020
This course has given me everything that one can expect to learn from the field of Image processing models like CNNs, Deep Convolutional Models like Inception, VGG-16, VGG-19, ResNets, etc. Other topics were also learned that included me applying these concepts into real-world applications like the neural style transfer as well as the object detection and face recognition.
Dec 02, 2017
The teaching style of Dr Ng is excellent as usual. He is able to take a complex topic and make it easy to understand. I found this course more challenging than the others in this specialization. It does require a bit of tenacity in order to finish the assignments. This is usual when coding. So don't give up and be sure to search the discussion forum when you hit a barrier.
автор: Esteban C•
Oct 08, 2019
Very good in-depth coverage of conv NN.
Just one little thing, week 4 Notebook assignments:
In style transfer code is not well explained how the train is actually working. In this case the input is set as a Variable instead of a Placeholder and this aspect is not mentioned or explained
In face recognition I still don't know how triple loss function is used during training
автор: WALEED E•
Mar 03, 2019
This course was the best I have ever taken. It gave me a big boost to carry my PhD research in robot vision with confidence of understanding what is happening all over the network and comprehension of one of the pioneer papers published in discussed in classes. Coding directly after finishing each week was the best to go to practice and apply all this knowledge gained.
автор: Kseniia P•
Jun 30, 2019
Amazing course with clear explanations of how CNN works. Andrew gives you intuition and understanding of convolutions, pulling, padding, and explains the foundations in great detail, so you can understand state-of-art approaches and are ready to get hands on it. Thanks to the assignments' structure, you don't ever have to waste time on debugging irrelevant issues.
Apr 06, 2018
I love this course. I only wish there was an opportunity to go step by step from looking at images, creating the dataset from the images, creating labels, applying a model, and then testing. This would help to answer a few questions that I have. However, when I read the papers recommended, I assume many of those questions will be answered, such as : why max pool?
автор: Umendra C•
Jan 11, 2018
Best course on deep learning for computer vision! Convolutional networks can be tricky to understand, but Andrew has presented the material in a very easy to understand format. He starts with simple ideas and concepts and then build on them in an intuitive manner. Highly recommended course for anyone who wants to understand the deep convolutional neural networks.
автор: Michal M•
Feb 11, 2018
Excellent course. Time well spent.
Simple explanations of difficult concepts.
I was able to download yolo v2 in pytorch, reconfigure it to use CPU on my Mac, and get it running on my webcam in 1h after completing Week3 assignment.
Told all my friends how awesome the course is.
Keep up the fantastic work.
Super stoked for part 5!!! and learning GANs and RI afterwards.
автор: Peter D•
Nov 26, 2017
Great course from Andrew Ng, as always. The videos are superb in explaining some of the more recent algorithms and trends. And they provide good intuition on how to use them in your own work.
The only (minor) remark is that the exercises might not be that challenging for those that already have done some ML programming in the past.
But overall still 5 stars!!!
Apr 15, 2019
I was always curious about the "CNN" concept every time it emerged in the news. Thanks to Prof. Andrew's mild explanation, now I get a straight intuition into it!
The assignments were very amusing in this section. It was not hard to get a pass with the help of forums, but understanding every step is more important I think. So I will come back to practice more.
автор: Sherif M•
Apr 19, 2019
Again a great course by Andrew Ng and his great team. Convolutional neural networks are the reason for the recent Deep Learning revolution or let's say better renaissance. Andrew does a great job in explaining the theory, math and application fields of CNNs while also telling about the history of recent advances in CNN algorithms and architectures.
автор: Jaime M M•
Jun 15, 2019
As in previous courses, Andrew made understandable complex and abstract content. This course is by far more challenging than the 3 previous ones. Maybe not at the assignments as we make use of facilitating frameworks and helper functions, but to really follow what is happening behind... its another level compared to previous courses on the specialization.
автор: Adrien S•
Dec 28, 2017
Great overall course, keep teaching please ! I learnt a lot. I have a Ms degree in Machine Learning but we didnt had the time to really learn about Deep Learning. I feel it was a great introduction to the field and I feel confortable now to get more in details about everything and read papers etc.
So thanks for that, and I can't wait for part 5 about RNN