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Вернуться к Convolutional Neural Networks

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

4.9
звезд
Оценки: 40,514
Рецензии: 5,371

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.

AR

11 июля 2020 г.

I really enjoyed this course, it would be awesome to see al least one training example using GPU (maybe in Google Colab since not everyone owns one) so we could train the deepest networks from scratch

Фильтр по:

526–550 из 5,347 отзывов о курсе Convolutional Neural Networks

автор: Divyansh R

29 авг. 2018 г.

Interesting and has depth. He'll take you to the complex parts without being bored. Perfect to binge on. Although I do recommended that you do the second course in the specialization for this course to make much more sense.

автор: Francis C W I

21 дек. 2017 г.

Great course! The hints for writing the code were very useful! Thank you for having mentors support the forum and thank you for emphasizing the intuitive understanding of Constitutional Neural Networks during the lectures!

автор: Sebastián R M

2 дек. 2017 г.

This is probably the best material on Convolutional Neural Networks and computer vision. And I've seen a lot. If you could only take one course on the subject, take this one. If you plan on taking many, take this one first.

автор: Piyush M

13 дек. 2017 г.

I would like to access the course assignments evene after I have completed them. So that I can use them for reference in future. Is it possible to make that happen or can i get the submitted jupyter notebooks mailed to me?

автор: Josef O

21 нояб. 2017 г.

There were some little bugs in the grader, which was sometimes a bit frustrating, but overall I learned some super cool state of the art stuff and the explanations in the videos (maybe except for the YOLO) were very clear.

автор: Salman A

7 февр. 2021 г.

This is also a wonderful course in Data Specialization series. All the complexities of CNNs are presented in very intuitive and simple manner. The assignments were also very helpful. Thanks Coursera for bringing it to us!

автор: Jeremy O C H

5 июня 2020 г.

A little bit more detail about the YOLO network like how the bounding boxes contribute to the training process and how it speeds up the iterating process would be very much appreciated.

Thank you for the wonderful course!

автор: UMESH S

19 мая 2020 г.

This one was the best course that I have done so far in the deep learning domain. I would recommend this course definitely if you want to learn from the conceptual background of CNN to its application in computer vision.

автор: Armin F

16 апр. 2020 г.

Excellent course on CNN concept, evolving architectures, and applications of classification, object detection, and image verification and recognition. Coding are fun. Student get to play with more packages and libraries.

автор: Rakesh G

30 авг. 2019 г.

for me, Andrew's comments were most valuable part of the course.

the exercises are just right for introduction and to encourage learning and progress towards completing the course while not getting bogged down with detail.

автор: Andreea A

3 мар. 2019 г.

The course has a lot of good content and the programming assignments are interesting. The course actually describes the various architectures of CNN's and the reasoning behind them. It still has some video editing issues.

автор: Wahyu G

20 апр. 2018 г.

I learned a lot in this course. The course explains some papers in an easy-to-understand way. Really recommend it for those who wants to explore the world of deep learning. I learned a lot! Thank you deeplearning.ai team!

автор: Alex B

6 дек. 2017 г.

Great overview of basic CNN concepts and history, with pointers to relevant articles.

On the downside the automatic grader for some assignments was not set up correctly, which caused unnecessary time waste and frustration.

автор: J A M S I

30 июня 2020 г.

Absolutely amazing course. Highly informative an nice explanation about convolution operations and CNN algorithms. Recommended for all beginners and intermediate levels who really want to know something deeper about CNN.

автор: Brynjólfur G J

9 нояб. 2017 г.

Some kinks in the video cutting of Andrew when he makes mistakes, but otherwise very good course. Didn't know anything about convolutions before and am fairly confident in my ability to at least read papers about it now!

автор: Jayashree V

26 авг. 2020 г.

I really enjoyed learning online course on Convolutional neural network. Thanks for the great instructors for having made such a great learning content keeping in pace with the recent research papers in CNN.

Thank you.

автор: Sen C

31 дек. 2019 г.

Discussion and reading on existing CNN architectures/frameworks was very interesting. Got to know a whole new collection of research and implementation in the field of CNN. And as always, Andrew NG is wonderful teacher.

автор: Christopher W

6 сент. 2019 г.

The most challenging to date - but clearly expected. This course is fascinating and demonstrates the true power of Deep Learning with respect to things like Facial Recognition and Neural Style Transfer. Amazing stuff!

автор: Gianluca M

16 мар. 2018 г.

That's what I came here for! Lots of applications on convolutional neural networks, with many ideas explained. As usual, I wished he would give us a little bit more details, but this is anyway a really brilliant course.

автор: Daniel B

16 янв. 2018 г.

Best course I've ever taken on CNN's. I now feel confident to attempt building computer vision applications of my own. Thank you to everyone at the deeplearning.ai and Coursera teams for making this material accessible!

автор: Shravankumar S

29 июня 2018 г.

I'm on week 3 right now and have completed previous 3 courses. I'm loving it very much, assignments are proving to be very helpful as you have simplified the process by providing markdown comments :) Thank you soo much

автор: moataz c

21 февр. 2018 г.

very informative i liked the learning style very much despite the system bug in garding i gave 5 stars because i encourge this Prof Andrew and his fellows to keep on the good work and provide more courses in the future

автор: Thomas B

3 дек. 2017 г.

Fantastic course. I found the course material for CNNs and their applications to 1d and 3d models fascinating. The projects were challenging and informative. I am looking forward to the next course with RNNs and LSTMs!

автор: Matías L M

4 дек. 2017 г.

The best course (at least of the four courses that I have already taken) in the Deep Learning Specialization. The topics are perfectly explained. The only problem is that the final assignment has a bug in the testing.

автор: Antonio C

26 апр. 2020 г.

Like the pervious courses, Convolutional Neural Networks is very useful to get you in touch with the new upcoming neural networks architectures. I particularly enjoyed the last course on triple loss and neural style.