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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:

501–525 из 5,347 отзывов о курсе Convolutional Neural Networks

автор: Ernesto N S

20 дек. 2017 г.

I love how the content of this course is structured. Also love the fact that all the weeks contents can be found within the exercises themselves. Thank you to all the people that worked so hard to deliver this exceptional content!

автор: Siddharth P

24 мая 2020 г.

A very well presented course that covers a good breath on different types of CNN model to date. The exercises are good, given the computational limitations, it is understandable why most of the exercises used pre-trained weights.

автор: Makarand D

19 мар. 2020 г.

A more consistent Keras or Tensorflow workflow would be good. I passed all the assignments but still feel unclear abotu Keras and TF workflow. But that will come with practice. Great teaching in terms of conceptual understanding.

автор: Zhoutian F

15 апр. 2018 г.

The course is really helpful to beginner. However, I really suggest to add more introduction of modern CNN networks to this course, such as R-CNN for semantic segmentation. Really appreciate Prof. Ng and Coursera for this course.

автор: Jose G P R ( B

19 дек. 2017 г.

Step-by-step convolutional networks are presented. It is excellent learning to construct the convolutional networks in the lab. I read too much about these type of neural networks, but no one has me shown before how to build them

автор: nitin k k

15 дек. 2017 г.

great explanation, great examples , great assignments , nice support and the most important thing is how easily andrew sir has explained the complex topics of deep networks . Its an honor to learn from such a great mentor. Thanks

автор: Jean M A S

10 дек. 2017 г.

It's surprising how the team deeplearning.ai stay consistent in the quality of the course given. Highly recommended. Maybe the best course in the specialization so far, Pr Andrew Ng has a gift to make something complicated plain.

автор: Pushpendra G

3 окт. 2020 г.

We can not describe how awesome these courses are! We are very much fortunate to see these time where everything is available and high-quality material can be accessed from anywhere. Thanks to Coursera Team and to Andrew Ng Sir.

автор: Evan C

28 мая 2018 г.

Allows anyone with a high school background in math to understand ideas behind some of today's most prevalent technologies. Thank you for taking the time to create this course from someone who now knows what to have a career in!

автор: Edward T

12 июля 2020 г.

Fantastic introduction to CNNs. Made me comfortable with the concepts and math regarding the various architectures. No doubt, when I revisit these concepts in the future, it will come back rather easily. Computer vision is fun!

автор: Alex H

13 июня 2018 г.

Some rough spots here and there with the homework grader, but overall the most useful course I've taken on Coursera in terms of exposure to new ideas from a different domain (images) that I can transpose to my own field (text).

автор: Chunduru G V S P

25 мая 2020 г.

Convolutional Neural Networks are very interesting. Andrew ng explained in detail with good assignments helped a lot. This course covered some of the emerging sectors in present world using research papers. Thank you Coursera.

автор: Piyush A

13 окт. 2019 г.

Again an amazing course taught by Andrew Ng. The way he explains the basics of the concept and the lecture slides is very helpful. Also, I loved working on the assignments in this course. Thanks for making this amazing course.

автор: Utkarsh K

13 окт. 2019 г.

This is the best course available for CNN. Even better than some of the university courses. Recommend doing the assignments properly to get full grasp on CNN. This has helped me clear so many concepts and get started with CNN.

автор: Md Z S

1 дек. 2018 г.

This course handles the crux of ML application of CNN network in Computer Vision. It is also very strong in math and concepts are explained well. I got to learn not only the concepts but also how strong is the research on CNN.

автор: XiaoLong L

5 нояб. 2017 г.

After about three days learning, I finished the fourth course and It's awesome. It's really easy and impressive. I appreciate what Prof. Andrew and the TAs did for this course. I'm looking forward to learning the final course.

автор: Dasharath A

11 июля 2020 г.

By taking this course, I got a chance to learn the fundamental concepts in CNN, and most importantly, many concepts in Computer Vision. If you want to work/research in computer vision, this course will give you a strong base.

автор: Kazım S

11 мар. 2019 г.

Course was well structured, and easy to follow. It also covers recent developments and famous papers, which was the best part for me. Many thanks to Coursera and prof. Ng for preparing and teaching us such valuable materials.

автор: Alexander G

22 мая 2018 г.

Thank you so much for your job!

That's great practical oriented course which helps me a lot to understand lots of mainstream and basic ideas and intuitions! Good and clear explanations and one of the best exercises! Thank you!

автор: Lambertus d G

24 нояб. 2020 г.

I enjoyed this course very much. It found it also easier to understand than the first 2 courses in this specialization. Maybe it is that I'm getting more familiar with the code and neural networks. It was definitely more fun

автор: shawn w

26 апр. 2020 г.

Absolutely loved this course. It is a great blend of updated CNN knowledge with challenging (but not unrealistic) programming assignments to actually learn and develop CNNs. Thank you Andrew (and company) for a great course!

автор: Sarfaraz K

15 февр. 2019 г.

Its an absolute gem of a course. Great explanation of state of the art CNN architectures in a very simplistic way. Loved every bit of it and learned a ton more. This course is a must do for anyone from beginners to advanced.

автор: Harri P

24 дек. 2018 г.

This course was quite challenging, but very rewarding! After completing this course I think I have a pretty good basic understanding of convolutional neural networks and their applications. Andrew Ng is an excellent teacher!

автор: David A M M

6 июня 2020 г.

It was a very complete and straight forward guide about everything you need to know about CNN's. Thank you so much for this impressive and fascinating course. I really enjoy it. I felt such if I acquired a new Jedi power :D

автор: Virginia A

7 апр. 2020 г.

This course satisfied and clarified absolutely my learning expectations around the subject of CNN.

Having a personal background in research I enjoyed the proposition of reference papers in each video as voluntarily reading.