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Вернуться к Convolutional Neural Networks

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

4.9
звезд
Оценки: 39,878
Рецензии: 5,273

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.

RS
11 дек. 2019 г.

Great Course Overall\n\nOne thing is that some videos are not edited properly so Andrew repeats the same thing, again and again, other than that great and simple explanation of such complicated tasks.

Фильтр по:

5126–5150 из 5,245 отзывов о курсе Convolutional Neural Networks

автор: Karan D

7 янв. 2018 г.

there were bugs in the jupyter notebooks

автор: Mohammad A

19 сент. 2020 г.

programming assignments are not helpful

автор: eric v

19 апр. 2018 г.

some of the quizzes were a little buggy

автор: Walid M A

17 нояб. 2017 г.

I did not like the assignments of w#4

автор: Pakhapoom S

14 мар. 2021 г.

The videos need to be edited properly.

автор: sai d s

17 янв. 2019 г.

Little bit hard programming Excercise

автор: Xirui Z

7 апр. 2021 г.

Too hard for someone new to tf.

автор: Sanskar j

18 июня 2020 г.

Assignments can be made better

автор: Jisheng L

15 июня 2018 г.

Need improvement on assignment

автор: Pedro C

10 июня 2018 г.

notebook were not functional

автор: Modassir A

11 мая 2020 г.

need improvement of content

автор: Olatunji O

12 февр. 2019 г.

Notebooks are a bit buggy

автор: Yi-Hao K

20 янв. 2018 г.

Serious bug in assignment

автор: Yide Z

13 янв. 2018 г.

too many errors in test

автор: akshat

25 июня 2021 г.

Labs should be tougher

автор: KevinZhou

8 мая 2018 г.

部分内容讲的不是很清楚,有些剪切不好,有重复

автор: Kenneth C V

4 дек. 2020 г.

Very complex Subject

автор: zz

5 мар. 2018 г.

没有翻译 tenserflow也讲得不好

автор: Pavao S

2 мар. 2018 г.

Not enough theory

автор: neda m

22 июня 2020 г.

too theoretical

автор: Volker H

16 дек. 2017 г.

too many bugs

автор: Shimaa

30 авг. 2021 г.

so hard :(

автор: Logos

27 авг. 2020 г.

It was okay. Andrew is obviously very knowledgeable, and there is a wealth of knowledge here. I could go through it a couple more times and still pick up new stuff.

That being said, I've heard him mention he did these videos at like 1 or 2 in the morning after work, and it's very obvious from the videos. He makes so many mistakes that every other lecture (it seems like) has a **CORRECTION** notification next to it. I mean it's great they give this additional correction information, but it would be even better if you just redid the video.

Furthermore, he like stops in the middle of the videos and then repeats the last sentence he said, because he made another mistake. I get it, Andrew is very successful, he's very busy, and I am definitely grateful for the knowledge he's provided in this course. But this makes for a very poor learning experience, because I'm taking notes, and I have to go back and redo them, plus the general angst you get when you're learning something and someone's like "oh wait nope that's not right, forget that." Well for God's sake I already learned it.

Finally, the submission assignments are the most annoying things I have ever come across. They are riddled with errors and misguided information where they literally tell you to use the wrong parameters, and then they never fix it. You have to go into the discussions to find out why your code is wrong, even though you're doing it right.

Then, you'll get everything right on your code for the test cases, and when you go to submit it fails you. And when I say it fails you, it gives you a literally 0 out of like 30 points. And the grader output just says "your submission was incorrect" like no way, I had no idea. Thank you for that very **cough** helpful piece of info.

If you go to the discussions, you find out this is actually a problem with how the grader is built, because if you don't format your code exactly the right way, it fails you, even if your solution is correct. I don't understand why it can be right when you run test cases, but submitting it fails.

Overall, I give it 3 stars before the poor grading, but because of the poor grading performance I have to bring it down to 2. I can't tell you how much time I wasted trying to figure out why my code was wrong just to realize it was right, but they screwed up their implementation.

In conclusion, this reminded me of a college course, where the professor has a ton of knowledge and is in high demand, and doesn't really care whether you get anything out of the course or not. It's sloppy, doesn't seem to be maintained very well, and most of the mentor's responses are literally "did you look at your colleagues similar questions?" Like no I didn't, that's why I'm asking. Why am I paying you so I can spend more time debugging your screw ups? Or maybe I did and I still don't get it because your explanations are ridiculously unclear.

I have one more course in this specialization and I absolutely can't wait for it to get over with so i can move on to more productive (and immersive, since these exercises are just one off "do this then do that" instructions, I still don't know how to set up a Deep Learning project from scratch) ways to learn Deep Learning. If Andrew wasn't so knowledgeable about this topic, I wouldn't even take it because it's that bad. But really you can't get this type of knowledge in such a condensed form anywhere else.

автор: Juan R

15 февр. 2018 г.

I found it very easy to go through the assignments and the quizzes were great, but I do have 2 complaints: -- I didn't get quiz feedbacks (they seem to be disabled), so, this is a huge let down and I wasn't able to completely grasp the concepts. -- For example the Gram matrix I had to accept it was true when they said "if the filters are quite similar then the dot product will be high". Show this please? #mastery #selfcontained. -- Another example, on the programming assignment, on Neural Style transfer, it is POORLY explained how the framework works when it comes to setting a_G and a_C. Then it is said "this will be covered (explained) in the "model" function, which wasn't. -- I have printed most of the mentioned papers and I am starting to read them, I loved the fact you recommended papers on this lesson, and the rest of the programming assignments were great, especially when you would provide "Hint" to go to the docs and lookup the method, etc.

автор: Jeff N

12 апр. 2018 г.

I feel this is by far the weakest of the first 4 courses in the series. The information is really valuable and the homework offers almost no opportunities to actually explore CNN architectures. The homework is more about implementing a few parts of a dictated network where all of the critical information is provided. The only exercises are in more vector manipulation and knowledge of frameworks that are never talked about in the actual course material. I'd love real framework material and real opportunities to practice using them, but the limited exposure here does not cut it.

Basically, I listened to the videos talk about CNNs, answered quiz questions about minor foot notes in the lectures, and then messed with vectors again. Oh, and the video editing was pretty choppy in this course compared to the others. Disappointed.