This was a very decent exploration of how Convolutional Neural Networks are used to solve various computer vision problems. The one complaint I have is that I wish the course wouldn't assume so much familiarity with Tensorflow and Keras frameworks in the assignments. The brief exposure to these frameworks earlier in the coursework is hardly sufficient to prepare one for the later assignments.

Overall this is a great course. I learnt a lot from this course, whether in conceptual aspect or practical. But I think it would be better if assignment about neural style transfer include model training as well. The training doesn't need to be done in high epochs and large data, using small portion of data and in small number of epochs is enough. Just for practicing how to optimize the model

Great course, the best CNN explanations I've seen so far on the internet. After finishing the course I have much more deeper understanding of convolutions. It is very helpful that we must code convolution neural network by hands with numpy as it greatly helps to understand the problem. The state-of-the-art examples are very interesting and helpful also. Loved to see Keras and tensorflow here.

Definitely 5+ stars. You got some much precious experience to implement those start-of-the-art deep learning applications with so much detailed explanation, supportive peer learners. It's really impossible for anywhere else to provide you this package to learn CNN, INN, YOLO, NST, FaceNet and so on so forth. I'm so grateful for the heart the teaching team pours into this course. Thank you.

Comencé esta especialización sin conocimientos de deeplearning en absoluto, hoy habiendo finalizado la especialización tengo una basta noción de este mundo tan apasionante. Quiero destacar la facilidad con la que Andrew transmite su conocimiento, es un instructor de otro mundo!. Feliz de haber realizado la especialización y de continuar por este camino. Gracias a Andrew Ng. y a Coursera.

As in all the previous courses in this specializations, Andrew Ng teaches the basics of neural networks in a clear, easy to understand manner. The programming exercises give nice hands-on examples of how you can apply the models described in the lecture, teaching both how to program the algorithms from scratch, and how to use higher level packages like keras and tensorflow. Great course!

This was my favorite class of the specialization so far. We've finally built up to the point where we can do some of the sexy things deep learning is known for. I have to say, I'm getting sick of having to submit every assignment 2 or 3 times and waiting for up to 2 hours to see if I passed because the Coursera grader doesn't want to work properly, but that isn't the instructor's fault.

The course is amazing and the topics are quite interesting. I think the explanation about the YOLO algorithm could be slightly better (I have to check out some external resources to catch the idea). Some programming assignments were hard to accomplish due to a lack of clarity about how to use TF. Nevertheless, all the topics covered along the course were really interesting. Thank you!

Amazing course, with careful explanations and intuitions for every algorithm. Beyond explaining greatly what are Convolutional Neural Networks, the course uses recent research papers to go through high level algorithms for face recognition and presented really nice applications such as Neural Style Transfer. I'd like to really thank the instructors for delivering this amazing course.

Es un curso que te enseña los fundamentos, técnicas y variaciones de las CovNets (Redes Neuronales con Convoluciones). Este curso es bastante bueno para introducirse en el mundo del análisis de imágenes y otros campos que utilicen datos no estructurados. Muy recomendado el curso, pero vean primeros los otros cursos de esta especialización para que pueden entender mejor los conceptos.

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.

Awesome CNNs course! I don't know why so many bad reviews, the grader doesn't fail if you follow the instructions (grade your assigment when you are asked!...tensorflow can only run one session so if you try to overwrite your model session with the teacher example session, grader will fail...tensorflow fault not this course) Would have love some GAN Week 5 Neural Style Generation :D

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.

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!

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

Really amazing and in-depth course. There is no better course than this to uncover the secrets of Deep Learning in the field of Computer Vision and how to easily utilize, improve and develop these systems. I am truly impressed by the content and by the knowledge I have gained and I doubt any university or other course can match up to Andrew's level of knowledge and teaching method.

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.

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 !

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 :)

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.

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.

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! :)

Introduces you to some of the key models and ideas in deep learning based computer vision (ResNets, inception models, network-in-network, region proposals, anchor box based object detection, semantic segmentation). Offers practical exercises which allow you to test your understanding by building/using these models. The only drawback - assignments have too much handholding.

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 .

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.