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Вернуться к Convolutional Neural Networks

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

4.9
звезд
Оценки: 39,693
Рецензии: 5,251

О курсе

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....

Лучшие рецензии

AG
12 янв. 2019 г.

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.

OA
3 сент. 2020 г.

Great course. Easy to understand and with very synthetized information on the most relevant topics, even though some videos repeat information due to wrong edition, everything is still understandable.

Фильтр по:

226–250 из 5,222 отзывов о курсе Convolutional Neural Networks

автор: balaji

24 дек. 2017 г.

As a beginner I have learnt a lot of topics with good clarity. Assignments have given me good understanding of the topics learnt.

I think the assignments should some more difficult and students should be able to spend some more time understanding the code and writing code of their own.

Thank you very much for making learning affordable and easy.

автор: William v

7 дек. 2017 г.

The libraries needed such as tensorflow, might need to better support (a special segment on them beyond the overview). Those models are complex and deep and using those libraries wasn't clear to me even though I managed to get the solutions, I needed time to study those libraries and they are rich and complex. I enjoyed the course immensely.

автор: Wanda L

15 февр. 2020 г.

Fantastic course about Convolutional Neural Networks! For me the best part of the course (and the specialization, too) is the assignment. You could hardly find a similar friendly, supported and easy-to-follow homework elsewhere in the world, even in some universities. Thanks to Andrew, and thanks to all teaching assistants in the community!

автор: Eddy P

26 мая 2019 г.

All are pretty good! Except for the low speed while running the training process which I think have in fact hurt the course's completeness. Because we have skipped many important training processes and instead use pretrained models to save time. I suggest maybe we can collaborate with Google and put the programming assignments on the Colab.

автор: Tu L

7 нояб. 2017 г.

Another amazing course from Prof Andrew and his colleagues. I've had a very exciting time to get to know about various CNN architectures, as well as to be able to implement, even just small part of them, and to make them work in practice. Thanks deeplearning.ai team a lot and look forward to seeing other courses from you in the near future.

автор: Harshavardhan S

4 нояб. 2017 г.

Awesome Course...You have gone out of your way to make the programming exercise simple enough for beginners to get a taste of very recent algorithms. thank you for your effort. I really loved the course. And it has given me enough to get me interested in and capable of following Computer Vision literature on my own with greater confidence.

автор: Prakash M

14 февр. 2020 г.

Wonderfully designed course for beginners to know all about CNNs. Even experienced professionals can have all their concepts cleared not only in CNNs, but also in YOLO and it's applications in object detection. Thank you very much Coursera Team for all your efforts in making this course accessible to thousands of aspiring data scientists.

автор: C D S

26 дек. 2020 г.

Professor Andrew makes the most difficult concepts crystal clear. The examples that are taken are very much relatable. If one does not understand the concept, going back and listening again is very helpful. I wish knew about him years back. CNN can save several lives by predicting accidents or environmental disasters. Very powerful tool.

автор: Paul M

23 апр. 2020 г.

Your courses are really great. I love the simplicity of the explanations followed by very advanced notebooks. Thnak you very much for your work. I appreciate a lot ! Maybe one observation. Personnaly I find the notebooks too guided and easy. Maybe you could write less in the notebooks and more links like you do with Hints. Thanks again

автор: Yedhu K V P

29 июня 2018 г.

This course helped me to learn in detail about convolutional neural networks. I have heard of CNN, but this is the first time I am trying it out myself. It's interesting and fun to learn. I'm planning to do more projects using the ideas learned from this course. I highly recommend this course to any aspiring machine learning student.

автор: Muhammad M K

23 февр. 2018 г.

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

23 апр. 2018 г.

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

27 нояб. 2017 г.

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: Minsheng L

11 апр. 2020 г.

a really nice class. I learned different techiques like CNN, YOLO, and used them to do face recognition, style transfering.... This calss is comprehensive. I need repeating many time before I can really master all of them. Thanks for the instructors, and all the people who have contributed to this calss. I've really learned a lot.

автор: Irina M

2 апр. 2019 г.

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

15 авг. 2018 г.

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: K173664 S K

9 февр. 2021 г.

This was a great course, thousand thanks to sir Andrew ng who put a great effort in structuring and delivering the course in a way that is easy to be digested for professionals as well as for beginners. there are a lot of cov net courses in the market but the knowledge and understanding I gained from this course are unmatchable.

автор: Gabriel M

13 июня 2020 г.

A good course, i feel like it only grasps the surface of the subject, but very good, feels way too easy should remove the rails because it feels way too streamlined and gives you very little room to wiggle, but the video content was very good and gives you the tools to understand the papers and the investigation on the subjects.

автор: Hesham H

17 окт. 2021 г.

This among the rest of this specialization courses is the best.

A handful of loaded information, strong course materials, very intuitive quizzes, and the best practice programming, feasible for TensorFlow programming. Overall, I feel really grateful for taking this course, not to mention the rest of the specialization for sure.

автор: 김홍숙

7 сент. 2020 г.

As EXCELLENT as other courses in deep learning specialization.

Must do progamming assignment by yourself to get hands-on experience and deeper understanding of what you learned from lectures.

I would like to express my sincere appreciation to Prof. Ng and all staffs who prepared this excellent course and programming assignments.

автор: Felippe T A

21 мая 2020 г.

A great course!! The content was very deep and it was presented to us some important CNN. For me, for this course be better, it needs a final project, but I can understand due to the large amount of content. But, in general it is a great course, maybe the best available on the internet. Thanks Coursera, thanks DeepLearning.ai.

автор: Dinesh T

27 мар. 2021 г.

This was one of the most interesting courses. Fun part and what I loved most is learning about the Neural Algorithm of Artistic Style - Neural Style Transfer (NST) algorithm. Would love to spend lot of time doing much deeper into the algorithms and mathematics behind it, so that I could build something meaningful and useful.

автор: Jack W

10 янв. 2018 г.

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

21 февр. 2020 г.

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 S

4 февр. 2019 г.

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.