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.
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 Hermes R S A•
There is a dedication, from the professor and the team, to teach you the most recent developments, without skipping important introductory level concepts. Having a grasp on the Imagenet winning architectures was really rewarding. The only down side was the YOLO algorithm assignment, because the notebook was a little confusing and disorganized, but you ca get the key ideas from it. All in all, it was my favorite course on this specialization.
by JOSHY J•
This is the best course for those who are serious about Deep Learning and computer vision. Some of the features of the course are Well Arranged, Simple, give a deep understanding of the mechanism, etc. We will learn Image processing, Image detection, Object detection, Face recognition and face detection through this course. Weekly assignments in the course give hand-o experience with the popular deep learning frameworks and neural networks.
by Shuai X•
Prior courses are almost all covered in the Stanford Machine Learning Course, which is free. If you don't want to waste time going through what the Stanford Machine Learning Course can offer, then this is the point to start to subscribe. Though it estimates 4 weeks of learning is needed, you can probably finish this course in a week. Assignments on CovNets and ResNets written in Tensorflow and Keras are mostly very good and very useful.
by Ashutosh P•
This is a really comprehensive course by professor Andrew Ng. He dove down to even the smallest details, you'll realize this when you listen to the lectures carefully. Make notes of each lecture as it's a long course and there are lots of terminologies in which you could easily lose yourself, stranded somewhere in between lectures having no clue what he's talking about. All-in-all, it's easily one of the best courses I've done on CNNs.
by Azer D•
Course was so helpful to understand concepts of conv nets. Also i like that Prof. Ng prepared the course with related successful papers of conv net world.One thing that i'm not happy is Coursera's Jupyter Notebook hub which I usually have problem with user authentication. Because of that I saved notebooks to my local machine, worked locally, and after completing it pasted my answers to notebook. I hope problems will be fixed soon.
by Jon M•
Fun and yet challenging. More challenging than some of the earlier courses because there's more advanced concepts. Without the pre-written code some of the assignments could have taken a novice ages to figure out, but the assignments are written with the goal of only really focusing our attention on the new stuff that was discussed in the lectures rather than forcing students to figure out the details from scratch. Loved it!
by JP L•
Extremely well done. Great balance between hand holding/help from the forums and effort in learning. I certainly appreciate the fact that after the course, you are ready to run in the real world working on AI endeavors. They also use all the most recent and up-to-date tools en development environments like Python notebooks, Keras and Tensorflow which makes you immediately proficient working in AI projects. Kudos to the team !
by Souvik S B•
This is an excellent course and so far gives best understanding of convoluitonal Network and how it works. But the grading issues needs to be resolved. One thing I specially like about andrew NG courses is how it explains the basics and how algorithms are written from scratch for better understanding. Would be good if we could do the same for YOLO and Facenet.However the assignments are well designed for good understanding.
The course is very interesting but we will have to practice after all that and go through the github codes in detail!
I found the professor Andrew is very clear in his explanations, especially in his desire to visualize what there is behind this complex models.
On the other hand I found the part on the Yolo model a little less well explained especially with regard to the anchor boxes. But I'm going to dig deeper into this.
by michael z•
Probably the best course in the specialization and the best course online on ConvNets!
Very engaging and interesting assignments, which cover advanced topics in an approachable manner. teaches current technologies (Keras, TensorFlow). The course goes into some of the math but doesn't get bogged down in it. The course includes recent developments in ConvNets such as the YOLO algorithm, Neural style transfer, and FaceNet.
by Vipul S•
There are lot of things are happening in computer vision field and this course helped me in understanding the concept like convolution and their use in computer vision field. Practical advice like using existing open-source implementation or existing network architecture are really helpful.
Overall this course equipped me to understand the CNN and it's practical application in computer vision field.
by Praphul S•
Some exercises very interesting, especially the last week. Why transpose was required made me reflect on the first course's content that dimensions matching will be a very useful technique to debug. Some highlights were the need for the convolution and how it reduces the complexity. The pace of the videos was good and details were very well explained (along with references which encourages to explore more on interest).
by Tao Z•
Andrew and his teaching assistants made difficult course easy to understand. This is not trivial at all. The exams not only tested students' knowledge but also provide hands on experience on real models, which should be very handy when students want to implement their own AI solutions by themselves later on. Andrew is certainly an excellent teacher and an outstanding AI ambassador, besides being a pioneer in the field!
by Kévin S•
You will go deep into image recognition and image processing related to deep learning. As this course show how to use pre-trained model, I should expect to get a model-hub (like docker-hub) like somewhere... but no.
Also I'm not sure to be able to do the exercice outside the notebook, because there is a lot of 'import' and libs to make work. An 'annexe'/'optional' course on how to setup environnement could be nice.
by AVEEK G•
Superb course structure, the assignments beautifully complement the lectures and the amount of guidance makes it easy even for someone not too acquainted with programming. As a suggestion would have liked slightly organized detailed presentations which would help in reviewing the course material later by glancing through rather than going through the lectures. Over all an awesome course with great learning. Thanks
by Yuwen W•
De-mystified sophisticated topics as always. Thru this course, I get a good understanding of the concept and basic building blocks of CNN, and the idea behind object localization, face recognition, neural style transfer.
After this course, I feel there is still a big gap between understanding the concepts and using them in the real world. Will move on to the tensorflow specialization to get more hands-on practice.
by Mohd Z C A•
The lectures, quizzes and assignments are designed to help you to understand the topics, not to penalize you. Real-life applications really help me to understand the concepts and the underlying principles. Only one minor issue that I think needs to be addressed - the use of older version of TensorFlow. The latest TensorFlow is not backward compatible and causes major issue when I tried to run the codes locally.
by ANTHONY R•
Excellent course with sufficient detail to become instantaneously productive, but at same time more deeper appreciation of internals that must be mastered when beginning designs don't work. Good launch point for learning new DNNs that are part of open source. Much better than Tensor Flow courses that just want you to know how to use the tool. I am ready to tackle my application which is wireless communications.
by Leigh L•
This course is a wonderful journey for me. I can certainly apply CNN skills into some of very interesting fields. I have already begun to experience other styles to argument my son's photo. It is a great fun. The facial recognition technique is great to learn. I'm living in China now. Chinese government applies the FR into many public CCTV. It is interesting to observe how they are using it (to say the least :)
by Melvin M•
An incredible course about "Convolutional Neural Networks" and related applications to image data. A complete and in-depth course concerning the most important concepts and algorithms about Computer Vision. Furthermore, a fun implementation section which enables youto to create exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images.
by Akshay N•
Very well structured and informative course. Got to learn plenty of new things, as well as an intuitive understanding of ubiquitous applications like face recognition. The only downside is that for learners not having a hold of frameworks like Tensorflow, the assignments can be a little challenging to tackle. Nonetheless, it helped me glean a very comprehensive understanding of CNNs. Keep up the good work.
by Pui L H (•
This is a great series of courses. He made things really clear and easy to understand. The assignments examples are so clear and neat. I actually used many assignments as a building block of my machine learning projects in production. I really hope that Dr Andrew Ng will give another series of courses about machine learning again, especially in the reinforcement learning area and the latest technology.
by israel z•
I was a bit of a practitioner before entering the course, in the sense that I could use some things, but many concepts were cloudy for me (I use it because it works, but I was not sure of what was under the hood). After this course I managed to learn so much about the things I was using. I feel like I can make more customized models now and rationalize on the decisions of the building blocks of my models.
by Qiongxue S•
I learned a lot from this CNN course, notations, algorithms, tensorflow and keras application. I would strongly recommand to learn this course. It made me think a lot smart applications in daily life and know better about what artifical intelligence is. Of course this is far more than enough, and I will keep learning the related knowledge and reading more about NN. Thanks a lot for the excellent tutorial!
by Rohit K•
Hello Andrew, I am a big fan of you. Learning from your every course. Very unfortunate that I can do that remotely only.
One thing that I want to mention - Can we have lecture notes on coursera, just like the way used to in CS229 that we can read before coming to next lecture. I found that that was very useful in understanding when things get harder.
Thanks hope we can improve coursera in that matter.