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Convolutional Neural Networks(으)로 돌아가기

deeplearning.ai의 Convolutional Neural Networks 학습자 리뷰 및 피드백

4.9
별점
39,702개의 평가
5,253개의 리뷰

강좌 소개

In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more. By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

최상위 리뷰

AG
2019년 1월 12일

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.

RS
2019년 12월 11일

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.

필터링 기준:

Convolutional Neural Networks의 5,222개 리뷰 중 151~175

교육 기관: Pui L H (

2018년 5월 2일

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.

교육 기관: israel z

2021년 1월 13일

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.

교육 기관: Qiongxue S

2019년 3월 4일

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!

교육 기관: Rohit K

2019년 7월 6일

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.

교육 기관: Kocić O

2018년 3월 15일

This course is almost perfect. It gives all the intuition that one might need about ConvNets and it introduces you to the most exciting papers in the field gently and in a fun way. However, in my personal opinion backpropagation of ConvNets should be treated in more details even if that requires some mathematical rigor. One more argument to this is that it can always be made an optional video/assignment.

교육 기관: Atul A

2017년 12월 12일

Excellent course! One of the best courses on ConvNet; it is rigorous and yet fun because of the broad range of projects - from Object Detection to Face Recognition / Face Verification and Neural Style Transfer. Andrew Ng's hallmark is his rigorous and thorough instructions from first principles. I would highly recommend this course to anyone looking to dive deeper into deep learning and computer vision!

교육 기관: ANGIRA S

2018년 3월 31일

This can be like the journey where you start as an acquaintance to the CNN's and end as an intimate friend. The excellent thing about this particular course is that it'll introduce you to the seminal computer vision papers and Prof. Ng will also guide as to the difficulty level of the papers. Another amazing learning opportunity is the case study. The text is already online, but the learning is here!

교육 기관: Vitalija S

2020년 6월 30일

Loved it but just as others have noted, programming exercises could have more comments about what we are doing because I had to spend lots of time trying to figure out what the task wants me to do. In addition, many links provided in comments about tensorflow documentation don't work. But as I said, this course was amazing because it helped me to understand many important things about CNN. Thank you.

교육 기관: Rahul M

2018년 2월 14일

This is just exceptional. Making cutting edge research accessible to learners. Making tough concepts available and understandable to beginner/intermediate students is hard enough, but Andrew makes it look easy. Some optional assignments where learners do everything from scratch would be good preparation for the real world - maybe this can be part of a capstone added at the end of this specialization.

교육 기관: Bo M

2018년 1월 8일

Some teach so that you understand that they understand. Others teach so that you understand. Andrew Ng belongs to the latter category. The course presents detailed overview of convolutional neural network with concepts ranging from 1D, 2D and 3D convolution, through max and average pooling, to style transfer. All concepts are carefully explained, with great illustrations and easy to follow examples.

교육 기관: Apperson H J

2020년 7월 12일

Course was great (as expected, Andrew is a terrific lecturer) - but it has a couple of problems:

* There are several errors that are pointed out, but sould be fixed in the lecture

* The exercises should use a more recent (ideally current) version of tensorflow

* You need to provide a utility that allows students to download ALL of the material involved (even imagedata that is accessible by links)

교육 기관: K

2020년 5월 7일

this course taught me the intuition and application Convolutional Neural Networks in the field of computer vision , Face recognition, face verification and Neural style transfer. I am very much intrigued to learn apply face recognition model into my project this helped me to understand papers and the explanation of Andrew is wonderful the advises he give really helps use while building projects.

교육 기관: Travis J

2018년 5월 28일

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.

교육 기관: Lukman H

2020년 11월 22일

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

교육 기관: Ivan S

2018년 2월 24일

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.

교육 기관: Zhixun H

2018년 2월 23일

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.

교육 기관: Patricio G

2021년 10월 15일

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.

교육 기관: Lucas G

2017년 11월 5일

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!

교육 기관: Brandon K

2017년 11월 19일

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.

교육 기관: Pedro T

2020년 8월 20일

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.

교육 기관: Victor A M B

2020년 4월 7일

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.

교육 기관: Jason J D

2019년 8월 18일

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.

교육 기관: Pablo G G

2020년 9월 10일

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

교육 기관: Sriram V

2019년 10월 17일

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.

교육 기관: Nelson F A

2019년 8월 22일

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!