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deeplearning.ai による Convolutional Neural Networks の受講者のレビューおよびフィードバック

4.9
39,702件の評価
5,253件のレビュー

コースについて

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....

人気のレビュー

AG
2019年1月12日

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
2019年12月11日

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.

フィルター:

Convolutional Neural Networks: 5101 - 5125 / 5,222 レビュー

by Chengqian W

2018年7月31日

Some technical issues/errors in lectures.

by Patrick M

2019年2月8日

Too many mistakes in assignment material

by Karan D

2018年1月7日

there were bugs in the jupyter notebooks

by Mohammad A

2020年9月19日

programming assignments are not helpful

by eric v

2018年4月19日

some of the quizzes were a little buggy

by Walid M A

2017年11月17日

I did not like the assignments of w#4

by Pakhapoom S

2021年3月14日

The videos need to be edited properly.

by sai d s

2019年1月17日

Little bit hard programming Excercise

by Xirui Z

2021年4月7日

Too hard for someone new to tf.

by Sanskar j

2020年6月18日

Assignments can be made better

by Jisheng L

2018年6月15日

Need improvement on assignment

by Pedro C

2018年6月10日

notebook were not functional

by Modassir A

2020年5月11日

need improvement of content

by Olatunji O

2019年2月12日

Notebooks are a bit buggy

by Yi-Hao K

2018年1月20日

Serious bug in assignment

by Yide Z

2018年1月13日

too many errors in test

by akshat

2021年6月25日

Labs should be tougher

by KevinZhou

2018年5月8日

部分内容讲的不是很清楚,有些剪切不好,有重复

by Kenneth C V

2020年12月4日

Very complex Subject

by zz

2018年3月5日

没有翻译 tenserflow也讲得不好

by Pavao S

2018年3月2日

Not enough theory

by neda m

2020年6月22日

too theoretical

by Volker H

2017年12月16日

too many bugs

by Shimaa

2021年8月30日

so hard :(

by Logos

2020年8月27日

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.