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Вернуться к Convolutional Neural Networks

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

4.9
звезд
Оценки: 40,627

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

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.

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:

4601–4625 из 5,361 отзывов о курсе Convolutional Neural Networks

автор: Georgezhu

7 апр. 2021 г.

Pretty good! Although this class is very good and easy to learn, something in this course is so simple that some key points are not well claimed.

автор: Suchitha L D

26 мая 2020 г.

Good content but the labs should be upgraded to Tensorflow 2. Some quiz questions are bit vague and feedback isn't helpful to understand mistakes

автор: mahdichalaki

26 мая 2020 г.

Some videos are not carefully edited and there the teacher repeats some sentences.

But overall, the materials and of course Andrew Ng are perfect.

автор: Arturo M R

29 авг. 2018 г.

Assignments are not always consolidating acquired knowlegde, due to the distance between implementation from scratch and using predefined models.

автор: Michalis F

16 февр. 2018 г.

Very nice course.

assignments are very high level though and just help in giving a taste of convolutional neural networks.

lecture notes are great

автор: Roberto J

21 нояб. 2017 г.

A bit buggy some of the exercises, some of the videos and some of the course notes, but the material is excellent and the learning is invaluable.

автор: Galley D

2 дек. 2017 г.

Amazing course that breaks down the complexity of CNN

Some assignment have issues yet the forum displays significant resources to help solve them

автор: Konstantinos C

29 авг. 2020 г.

Some key points for not giving 5 stars (lack of questions during lecture, assignments too easy). But, as always Andrew is an excellent teacher!

автор: Akshay K

30 окт. 2019 г.

The course is complex, it must teach more examples of coding and practice overall the content of this course requires very high level practice.

автор: Michael B

26 мар. 2018 г.

I really like the videos, and the content covered. It would be nice if the large blocks of videos were separated by some programming exercises.

автор: zlb

12 янв. 2018 г.

The coding assignment is a little hard for people who don't know much about tensorflow and keras. But on the whole, this is a brilliant course.

автор: Andrey Z

3 дек. 2017 г.

There is a great course for many people to begin working with the such an enigmatic stuff as cnn! Thank you very much for creating this course!

автор: Venkata R P

10 нояб. 2019 г.

Wonderful and well-structured course! Following instructions on jupyter notebook is confusing, had to go back and forth to do the assignments.

автор: Bourget R

28 окт. 2018 г.

Grading assignments troubles are annoying especially after such a good teaching it is really frustrating. nevertheless it's an amazing course!

автор: Bas v d B

30 дек. 2017 г.

Interesting course, but the assignments were sometimes disappointing. There were bugs/issues, and it wasn't very clear how to deal with these.

автор: Henry L

15 июня 2020 г.

The coverage of the material is excellent. The only downside is that there is little support for the exercises and can be quite frustrating.

автор: Xuanlong Y

12 дек. 2019 г.

I think perhaps you can add more detailed explaination on Fast R-CNN and so on. Also, I think the homework can be updated to Tensorflow 2.0.

автор: Андрій Б

20 нояб. 2018 г.

It's very difficult. I believe it can be more simple. But on the other hand maybe when I go through it 2 or 3 times it'll became more clear.

автор: Manan A S

19 мар. 2020 г.

Andrew could have explained a few concepts in depth overall a great course though giving an overview of CNN's & deep learning architectures

автор: Thomas A

4 окт. 2019 г.

Pretty good course, holds a lot of relevant information.

Though the going into every little detail of doing a convolution is a bit overkill.

автор: APOORV S

17 мар. 2019 г.

I think Keras should have been used more in assignments. Overall the concepts are illustrated in the videos perfectly as well as precisely.

автор: Ethan M

18 авг. 2021 г.

Instructions were clear and videos were well thought out. 4 stars because I had to reference the Documentation for Tensorflow consistently

автор: Priyansh J

19 янв. 2020 г.

this course had all the practice exercises and todo assignments with proper lectures and examples to understand even such complex problems

автор: Kai W

31 дек. 2017 г.

course is good, homework has bug in week4's homework, meeting expected output is graded wrong, changing to different output got correct.

автор: Rafał W

22 нояб. 2017 г.

Errors in grading procedure. In many lecture videos you hear that they are consist of many recordings that do not compose one whole thing.