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Вернуться к Convolutional Neural Networks

Отзывы учащихся о курсе Convolutional Neural Networks от партнера deeplearning.ai

4.9
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Оценки: 29,173
Рецензии: 3,561

О курсе

This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. You will: - Understand how to build a convolutional neural network, including recent variations such as residual networks. - Know how to apply convolutional networks to visual detection and recognition tasks. - Know to use neural style transfer to generate art. - Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. This is the fourth course of the Deep Learning Specialization....

Лучшие рецензии

RK

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.

AG

Jan 13, 2019

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.

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176–200 из 3,528 отзывов о курсе Convolutional Neural Networks

автор: Muhammad M K

Feb 23, 2018

An amazing course! Not only does the course covers seminal work in the area of deep learning related to image processing but it shares valuable insights into problem solving and provides hands on experience. If there is a single course that I have to recommend to anyone related to deep learning for image processing, this would be it.

автор: Rajthilak M

Apr 23, 2018

The lectures were excellent and helped me understand the key elements of convolutional neural networks. I enjoyed coding the assignments and building foundation knowledge for building real-world AI applications. Thanks to the very strong foundation ,I am able to read and interpret many of the real world AI experts' blog and views.

автор: Deleted A

Nov 27, 2017

This is really a superb course. Andrew Ng has the ability to clearly explicate the complexities of convolutional networks. The coverage of topics such as residual networks, face recognition, Yolo, and neural style transfer are both intriguing and informative. I found the programming assignments challenging, but deeply instructive.

автор: Irina M

Apr 02, 2019

Thank you for the course and I really like it. Learn a lot and I made few teaching sessions of DeepLearning algorithm for Women Who Code, where I am mentor in leadership group. I clarified many things for myself during the course, I very grateful for the amazing knowledge and experience! I will recommend this course to colleagues.

автор: Tun C

Aug 15, 2018

I appreciate the way professor Ng made the Convolutional Neural Networks concepts and architectures easy to understand. This course gave a very good overview and professor Ng presented the intuition behind the concepts as usual. The programming assignments are also a good mix of under-the-hood and high-level application of CNN.

автор: Wei W

Jan 10, 2018

This is a great intro to deep learning/AI course. Professor Ng has a way to explain things in a way that is super easy to understand. Basic knowledge (college level, but no need to be math/cs major) on linear algebra is required. If you are in science/engineer major, and took any kind of linear algebra class, you will be OK.

автор: keerthi k

Feb 21, 2020

Thank you so much Coursera. I have been doing this specialization properly, but suddenly I had an accident which took almost 10 days to become normal. During those time several assignments were overdue, but Coursera extended their assignments deadline twice and helped me complete this course. So once again I thank Coursera.

автор: Abhishek K S

Feb 04, 2019

The CNN is always found as one of the trickier concepts to follow and it was actually very hard for me to figure out what these Conv layers are doing. But this course is so robust and easy to follow that I was even able to read the research papers on advanced CNN architectures with relative ease. Thanks to Andrew and team.

автор: ANSHUMAN S

Jun 04, 2019

It has been a great journey through learning CNNs it was quite interesting rather than all other courses and I got to know really very new ideas which i can implement in my own models.

Once again I want to thanks Andrew Ng and all other teachers of Course

and a special thanks to Coursera for giving me this ample opportunity

автор: Nick H

May 22, 2019

Awesome course if you want to understand the basics of CNNs along with recent applications of these algorithmns.

As usual, both Andrew's material and his presentation style kept me both engaged and interested to a point that I got ahead of the weekly schedule...which is probably a good metric in terms of course success

автор: Keetha N V

Oct 20, 2019

Great course by Andrew Ng sir. It gives us a great insight into many case of studies of state of the art ConvNet. Gives us a lot of intuition about object detection systems in autonomous driving and landmark detection , one shot learning for face recognition and a fun way of applying ConvNets for neural style transfer!

автор: Wang F

Jan 14, 2018

Despite the confusing bug and server running problem in the last assignment of happy house ,

the course is still great . Compare to the other three ones, it's the hardest course for me by now .

You may feel stuck in some practice questions and program .Worth spending time to review the

stuffs of the course again。

автор: 杨建文

Jan 10, 2018

The last 2 courses were delayed, but the positive side for me is that, in the beginning I proceed too fast and didn't learn that well, the delay made me take more time on such a valuable course, carefully reading and memorizing the instructions of assignments. I'm really grateful for Prof. Ng's excellent instructions.

автор: Eric C

Jun 23, 2019

Awesome. This course was much more dense than the other ones, there is so many areas to review. Since this course is about my favorite subject, I will need to pause and rework on each individual points and associated papers (yolo, nst, similarity learning) which will probably take me weeks... Prof Andrew is the best

автор: Arvind N

Nov 03, 2017

I thoroughly enjoyed taking this course. Beautifully designed...Thank you!

I had written a detailed review of the first 3 deeplearing.ai courses at : https://medium.com/towards-data-science/thoughts-after-taking-the-deeplearning-ai-courses-8568f132153

I will review this CNN course as well, in the form of a blog post.

автор: Wade J

Mar 25, 2018

Good amount of challenge for after work learning. Nice exposure to different applications of AI. Fun.

Andrew Ng is awesome at explaining the concepts. Almost anybody would be able to understand them after he presents them. I also appreciate how genuine he is. You can trust that there is merit to what he tells you.

автор: Glenn P

Dec 10, 2017

Another excellent course. Convolutional Neural Networks is no longer a mystery. I like the fact that Andrew doesn't teach this as an academic class but has a very practical approach that can be applied right way. He lets you know the strengths and weakness of each of the NN and gives his personal opinion as well.

автор: Yijie

May 16, 2018

It is a great course that covers most part of Convolutional Neural Networks. I have learned a lot from it. Thanks Andrew! Only one suggestion: we have learned dropout and the batch norm in previous courses. Because they are such important tricks, it would be better if you could cover how they can be used in CNN.

автор: Ahmad B E

Nov 04, 2017

Greatest cores for me till now on deep learning. I recommend it for deep learner or computer vision student. The best thing in this course is that it is very practical and up to date, and full of research papers of algorithms that Google and Facebook currently uses. Thanks a lot Prof Andrew Ng you are the best.

автор: Parab N S

Aug 25, 2019

An Excellent Course to make people understand Convolutional Neural Networks in good depth and with ease. The detailed understanding of the major Convolutional models like YOLO and ResNet is like an icing on the cake. I would like to thank Professor Andrew N.G. and his team for developing this wonderful course.

автор: Alejandro M v G

Aug 06, 2019

Muy bueno para empezar a entender los conceptos de las capas convolucionales. Luego muestra modelos profundos como AlexNet, VGG16, ResNET, Inception que se pueden entrenar usando transfer learning. La parte de detección de objetos es la mas complicada. La parte que más me gusto fue la de reconocimiento facial.

автор: Alexandre M

Nov 29, 2019

One of the most important courses in the Deep Learning Specialization in my opinion. Good content, enjoyed the homework, lots of details for beginners and extra resources for more advance content. Would definitely recommend for anyone interested in working in Machine Learning especially in Computer Vision.

автор: Avineil J

Dec 04, 2017

Exceptional Course. Learnt a lot from it. Takes a different approach to teaching than other courses in the sense that more focus is on applications rather than training of models for which a GPU cluster is a must. Thanks Andrew Ng and his team for the wonderful course. Looking forward to sequence models :)

автор: Jizhou Y

Mar 08, 2019

Professor Andrew is really knowledgeable. The lecture videos he makes are really helpful for me. I really learn a lot from them. Also, the recommended learning materials such as academic paper he recommend are really useful for me if I want to further my learning on the residual network or YOLO algorithm.

автор: J.-F. R

Feb 18, 2020

Great course by Prof Ng. I had taken his Machine Learning course a few years ago, so expected high standards of content and assignment preparation - I was not disappointed. Staff is very responsive and helpful in forums as well. I highly recommend it. Taken as part of the DeepLearning specialization.