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Отзывы учащихся о курсе Convolutional Neural Networks от партнера

Оценки: 40,635

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


1 сент. 2019 г.

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.


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

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4576–4600 из 5,364 отзывов о курсе Convolutional Neural Networks

автор: Tianqi T

19 июня 2019 г.

the content of this course was very interesting and practical. however towards the end (week 4), there were a lot of confusions about how TensorFlow works.

автор: Pieter N

15 янв. 2018 г.

Excellent course. My only reason for not giving 5 stars is purely because there was a grading error on the last weeks' assignment which is still not fixed.


4 дек. 2017 г.

Really insightful. I did the course on Udacity and didnt understand much about the CNN but now I feel I have a better understand. I can't wait to apply it!

автор: Hao Z

2 дек. 2017 г.

The bugs in the grading system make me uncomfortable. People have to submit the answer which is obviously wrong but favored by the grader to pass the test.

автор: Hamlet B

19 нояб. 2017 г.

The content was fun and very useful. The last programming assignment had some incorrect guidance and made the grading experience unnecessarily frustrating.

автор: Jose-Luis L

26 нояб. 2019 г.

The course is great. There is only one minor downside: sometimes the notebooks' connection is lost and you lose the latest modifications of your homework.

автор: M J

2 июля 2019 г.

A good introduction to CNN's if you haven't seen them before. Strong on concepts and motivations. Kinda vague on mathematical details and implementations.

автор: Preethi G

6 мая 2018 г.

The course structure is really good. But please do fix the bugs in the assignments. Thanks to the discussion forum, it helped me a lot in fixing the bugs.

автор: Sudipto C

1 янв. 2018 г.

Excellent content delivered very honestly by Prof. Andrew Ng. The course has some broken grader issues which need to be fixed to make this course awesome.

автор: Jonatan K

6 янв. 2020 г.

explained very well

very interesting with andrew

the main problem with this course is bug fixing on assignments.. i lost alot of hours just because of this

автор: Kenji M M

13 сент. 2019 г.

the last programming assingment has a lot of bugs as of 9/13/19 and was verry difficult to pass even though the actual code was very simple ti implement.

автор: Yash R

24 дек. 2021 г.

It was a great course. Though the YOLO implementation part is a little confusing. Probably more implementation details could be covered in the lectures.

автор: Ameya G

21 дек. 2017 г.

Course content was good and well structured. Some videos still need editing and grader for 2 assignments is faulty. Otherwise a very interesting course.

автор: Nazmus S

4 авг. 2019 г.

ipython notebook fails often. It was a frustrating experience. There are many bugs to be fixed to run the homework problem submission process smoothly.

автор: itay k

21 дек. 2017 г.

A great course! I would have gladly given it 5 stars, but currently, the assignment of week four have bugs and the notebooks tend to stuck or run slow.

автор: Venkat K

5 дек. 2017 г.

A bit dense and fast-paced even for Prof Ng's usual standards - this course is drinking from a firehose, but a great hands-on introduction to ConvNets

автор: Nitish S

7 нояб. 2019 г.

Could have a better explanation of TensorFlow graphs in the assignments. The course is still very good and provides a solid conceptual understanding.

автор: Frank H

17 дек. 2017 г.

Very interesting topics were covered in a quite comprehensive way. Only useful packages like Tensorflow and Keras were introduced only superficially.

автор: Erik P

27 нояб. 2017 г.

Another wonderful course by the team, even with a few bumps this is one of the best introductions to what the heck a convolutional neural network is!

автор: Adithya J A

31 авг. 2020 г.

Great course and would totally recommend it. Assignments need a bit of work in terms of instruction clarity for use of certain tensor-flow commands.

автор: Pablo M P

24 авг. 2019 г.

I learnt many interesting ideas about convolutional networks, however, the course needs to be checked by the staff. There are many bugs in the code!

автор: Jarek D

19 мая 2018 г.

Programming challenges in this course were less practical than in previous ones, and instructions sometimes a bit vague. Still recommend it, though.

автор: Md R R J

25 июня 2020 г.

The notebooks did not help much to practice skills received from video lectures. Specially last 2 weeks. Felt like translating formulas into codes.

автор: Ernst H

5 авг. 2019 г.

Very good course. The assignments are too easy and I would be able to complete them without understanding the course material or what I am coding.

автор: Nitish K

21 апр. 2018 г.

Neural Transfer and Object Identification (YOLO) was not explained very well. I had to look out for external videos on Yoututbe to understand YOLO.