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Вернуться к Convolutional Neural Networks

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

4.9
звезд
Оценки: 28,583
Рецензии: 3,453

О курсе

This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. You will: - Understand how to build a convolutional neural network, including recent variations such as residual networks. - Know how to apply convolutional networks to visual detection and recognition tasks. - Know to use neural style transfer to generate art. - Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. This is the fourth course of the Deep Learning Specialization....

Лучшие рецензии

AG

Jan 13, 2019

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.

RK

Sep 02, 2019

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.

Фильтр по:

3326–3350 из 3,417 отзывов о курсе Convolutional Neural Networks

автор: sai d s

Jan 17, 2019

Little bit hard programming Excercise

автор: Jisheng L

Jun 15, 2018

Need improvement on assignment

автор: Pedro C

Jun 10, 2018

notebook were not functional

автор: Tunji O

Feb 12, 2019

Notebooks are a bit buggy

автор: Yi-Hao K

Jan 20, 2018

Serious bug in assignment

автор: Yide Z

Jan 13, 2018

too many errors in test

автор: KevinZhou

May 08, 2018

部分内容讲的不是很清楚,有些剪切不好,有重复

автор: zz

Mar 05, 2018

没有翻译 tenserflow也讲得不好

автор: Pavao S

Mar 02, 2018

Not enough theory

автор: Volker H

Dec 16, 2017

too many bugs

автор: Juan F R L

Feb 15, 2018

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

Apr 12, 2018

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.

автор: Jacob K

Sep 01, 2019

Great content, but this module gets far too buggy. The videos stutter and repeat as if they were going to be edited butt never were, and the programming exercises are so sloppy. The first exercise says, welcome to the second exercise, and congratulates you for finishing the course, even though the second assignment remains, that also says welcome to the second exercise! Loading a model hangs forever on one, and running the GAN crashes the kernel on the other. People in the forum have been complaining since at LEAST last year, and it's still buggy. This course content is great, but very shoddily put together compared to the rest. I am literally scared what week 5 will be like. Just clean it up guys. Hire an temp!

автор: Alexandre E

Dec 05, 2017

Course is great, but there were several bug in the homework, including misleading tests.

In one, getting the right value (triplet loss) results in a failing grade, getting the wrong values (using help from the forum) get you to pass the test. In another test, there were corrupted files; one has to add a print statement in a helper function, learn what file is corrupted, rename it, reload the exercise, and voila, it works.

Clearly, graders should survey the forum more closely to address these issues. Hopefully it will be addressed soon, and these comments will become moot.

That aside, the quality of the videos and the insight provided by Andrew Ng are second to none, thanks for the outstanding instruction

автор: Jacob T

Nov 29, 2017

Felt compelled to review this particular course to voice my dissatisfaction. The course, as it stands right now, is rather poor in quality. The lectures contain several errors that are lazily corrected. Sections of video are incorrectly spliced together that chops up the flow. The programming assignments drop sharply drop in quality from the previous courses; they're pretty close to "type the stuff we tell you to type" at this point. Even at that, there's several errors in those assignments that require digging into the forums because the course instructors seem to lack quality control.

I quite enjoyed this specialization in courses 1-3, but this course has left quite a bad taste in my mouth.

автор: Robert D

Jun 21, 2018

While the content of the course is thought provoking and up to date, the overall quality is quite low. Videos are of moderate quality with very poor audio editing, and the programming exercises suffer from poor auto-graders. Regarding programming assignments, I spend most of my time trying to get just right combination of function calls despite getting exaclty the right answer in my tests. Typically this comes down to using just right numpy or tensorflow function, despite either one giving the same results. Overall, I wouldn't recommend taking this course for credit but rather simply extracting the relevant lessons and recommended readings.

автор: Slobodan C

Dec 04, 2017

The lectures are quite interesting, but the course should be at least twice as long to cover the CNNs with enough depth for a practical application. For the assignments, the Grader and the Notebook worked terrible compared to all the courses I took on Coursera so far. There were many discrepancies between the Notebook and the Grader- code matching the expected output in the Notebook would fail in the Grader etc. Starting about two days before the assignment deadlines, loading models into the Notebook would take 30-40 minutes, and crash most of the time, with unreadable error messages. Files got corrupted, sessions ran for hours...

автор: Juan M

Dec 30, 2017

As with other courses from Andrew, the lectures were great - easy to follow, clear explanations, great insights, lots of practical advice. The main reason for the lower than average rating is related to all the issues with doing the programming assignments. There seemed to be a larger than usual number of errors in the notebooks and one in particular (Week 4) had a problem with the grader that persisted for several weeks (if not still ongoing). In addition, several of the assignments didn't seem to really help in understanding the algorithms for CNN but instead concentrated on the minutae of the frameworks like tensorflow.

автор: fheinrichs

Dec 01, 2017

This course presents some important state-of-the-art in convnets and teaches you everything you need to get your feet wet in that area. As always, Andrew is a great teacher. However, the programming assignments are a mess. Sometimes they are trivial, sometimes you feel completely lost. That wouldn't be a problem, if it were not for multiple bugs in the grader. So, after solving the task correctly, you find out that the grader expects an incorrect value and you have to figure out what mistake the developer might have made. Without the forum and very helpful other students, there is almost no chance of completion.

автор: Stefano A

Jun 05, 2018

Frustrating and annoying pitfalls in the assignements: most of the time you lose time on trivial syntactical issues on python / tensor flow, rather than concentrating on the model itself.

Beside that the Kernel stabiliyt is gettin worse and worse in these courses as the weight of the models increase: the kernel breaks too frequently and you don't have any other way to restart it from the beginning, losing all the modification.

It takes ages to reach the end for trivial issues, not related to the subject of the course

It is impossible to accomplish the grades without digging in the forums

автор: Andrew W

Dec 16, 2019

Material explained very well, but course material was very poor. To really understand the material one has to basically rewrite all the class notes themselves. Maybe this is a great way to learn, but it can take a lot more time than advertised. The jupyter notebooks are well done, and a great source for future reference. But the main problem is that only the notebooks can be downloaded. All datasets and pictures do not download using the provided coursera instructions. I called coursera, but the problem could not be solved. This was very disappointing and extremely frustrating.

автор: Peter G

Dec 10, 2017

Assignment for Week 3 is just a load of BS. Complete mess with no structured attempt to explain relations between suggested data-structures and built-in functions that use them. Whole fairly nice course is completely ruined by this one mindless pile of 'fill in random line of code to get the result' approach.

On the top of that - a final cherry on the pie was complete mess with Week 4 assignment on face recognition. Multiple bugs in the assignment code and grading, broken db's for the notebook and complete lack of support from Coursera. A shame. Weak and shame.

автор: Oliverio J S J

Feb 03, 2019

This course is an interesting review about techniques of image recognition based on neural networks. Unfortunately, it is not possible to achieve a deep understanding of these techniques during the time the course lasts. The practical activities are just filling lines in programs following the provided instructions and, sometimes, it is even possible to do it without understanding the rest of the code. The frequent disconnections between the notebook and the server slowed me down a lot and even made me lose all my work and start from scratch several times.

автор: Joshua O

Nov 14, 2018

The first couple weeks laid a good foundation for understanding CNNs, but I did not understand the point of diving so deep in to Computer Vision, especially having a lengthy programming assignment devoted to an algorithm as complex and relatively niche as YOLO. There are several different architectures/applications of Deep Neural Nets conspicuously absent from this entire sequence, most notably GANs and AutoEncoders. I felt a good deal of frustration when implementing the programming assignments in the latter half of this course

автор: Elias F

Dec 24, 2017

Overall it's a very comprehensive course with a broad set of topics which I found insightfull. However, the programming assignments, in particular the Happy House, was done in a rush due to errors in the models and code provided. Part of the assignment couldn't be tested just for the lack of access to the model and evaluated its results after its grading. The forums were also crowded with many threads talking about similar issues. Hope you can improve this section in order to create a more solid course.