Apr 30, 2020
An extremely good course for anyone starting to build deep learning models. I am very satisfied at the end of this course as i was able to code models easily using pytorch. Definitely recomended!!
May 16, 2020
This is not a bad course at all. One feedback, however, is making the quizzes longer, and adding difficult questions especially concept-based one in the quiz will be more rewarding and valuable.
von César A C•
Jun 25, 2020
The course is quite complete, but it contains to many things already contained in the previous courses within the Specialization. The final honor part could have been much better.
von Geir D•
Mar 08, 2020
Presenter is a synthesized computer voice. Slides and exercises are full of spelling errors. Contents is OK, but presentation is not very inspiring.
von Tony D•
Sep 08, 2020
Very slow and redundant material with previous courses of the "IBM AI Engineering Certificat Professionnel"
von Mutlu O•
Aug 04, 2020
More useful exmples in labs would be helpful to understand the possibilities with the method and tool
von Miroslav T•
Jun 08, 2020
quality of videos at the beginning of course are low, fells like the machine is reading it
von Benhur O J•
Jan 30, 2020
To focus in the coding but not the underlying structure of the library and how to use it.
von Prateeth N•
Jul 01, 2020
Very Basic course. Would have enjoyed more interesting examples in the notebooks
von Bhaskar N S•
Apr 04, 2020
Found it very difficult to follow some of the content and assignments
von Pakawat N•
May 05, 2020
There are a lot of mistakes in the slides and video but no updates
von Suman S•
May 03, 2020
The course is too heavy to have just one project.
Jul 15, 2020
A number of mistakes were found in the course.
von Juho H•
May 06, 2020
This course is difficult to rate as a learning experience. There are some very good parts yet there is also some very poor material. I would say that if you are already very familiar with machine learning and Python BEFORE taking this course, you can still draw some useful learnings on how PyTorch can be applied to various problems, and how to create convolutional neural networks with it; but if you are uncertain about some of the key concepts, this course may only end up making things worse for you.
To give an idea of the problems, there are issues like:
- When explaining the train/validation/test data logic and how validation data can be used to prevent overfitting, the videos keep calling training data test data.
- Pytorch is used for some really fancy stuff like defining functions and datasets, but then those functions are not parametrized in any sensible way – meaning if you want to compare loss functions from two different initialisations of the model weights, you are expected to define a new function so you can just change the variable “LOSS” to “LOSS2”, rather than just passing the loss function as a parameter or just initializing or returning it. Given the Pytorch logic is not your regular Python stuff, a best practice should be provided – it is definitely not writing a new function every time.
So be warned: if you know what you are doing, and simply want to learn how to do it with Pytorch, this may still be a decent course for you, just ignore all the stuff where the instructors make mistakes (and they are plenty, also in incorrect quiz answers). But if you feel at all uncertain, I suggest you hone your machine learning skills elsewhere, because otherwise this course will leave you totally confounded on even the very basics of machine learning.
On the upside then, you learn Pytorch through repetition. In the beginning, the logic appears very intimidating, but then you gradually learn the logic and you can do some very impressive stuff quite easily in the end. Be prepared for the amount of repetition, however - first the stuff is shown on a video, then you run the exactly same stuff in a lab, and unfortunately the Skills Lab is not at all efficient for some of the stuff - I ended up downloading the notebooks and using them on my Watson Studio account for much faster performance.
von Roger S P M•
Mar 31, 2020
The course material contains some really fantastic information, graphics, and programming assignments. However, the presentation of this material is absolutely terrible! It seems they intentionally tried to make the presentations as boring as possible. The lectures are monotone, the 15 second opening scene is annoying, and the content focuses 70% on the concepts of Deep Learning (which is fine) and 30% on PyTorch. So when you finish you do not feel very skilled with PyTorch.
Finally, ALL of the student complain that the programming environment is very often offline. You cannot do many of the assignments because the "Cognitive Classroom" is usually not working. However, the last lecture f each week contains the Jupyter notebooks for the assignments. You can download and then run them in some other environment like Google Colaboratory or IBM Watson Cloud. Also, most of the programs contain a programming omission that the students have to fix every time. The instructors have not fixed the problem which has been reported to them. So pay attention for the "Pillow Error" in Week 3 because you will be fixing it yourself in most assignments for the next 4 weeks.
von Ben A•
Aug 05, 2020
Awful quality content that fails to teach or test you properly.
The videos are exceptionally poor using a text-to-speech narrator that makes you want to quit after only one video. Additionally, the quizzes are buggy with awful wording, typos, invisible options, and useless content. The biggest shame is that they don't use notebooks to test your learning with real examples that would reinforce both the theory & practical elements.
This course has no effort put into it & is clearly a money grab. Avoid this and instead try a deeplearning.ai or fast.ai course.
von Calvin W Y C•
Mar 11, 2020
The general content of the course is good. However, I was experiencing a lot of problem accessing the lab platform. Also, there are typos and grammatical mistake everywhere in the quizzes. The audio of the video are done using computer generate voice over, instead of a real person speaking. I think the instructor of the course doesn't speak fluent English, which is understandable why computer voice over is used instead, but the non-stopping speech makes me a bit hard to concentrate sometimes.
von Iain G•
Apr 02, 2020
The quizzes are a complete joke. If you're hoping employers will take Coursera certificates seriously, the standard of assessment here is not good enough by a long long way.
von Victor B•
Mar 27, 2020
I found the course instruction is confusing, sequential and class module should be in different video parts
von Octavio L•
Aug 28, 2020
Me resultó un tanto tedioso y demasiado largo. Se solapa con contenidos de otros cursos del certificado
von jack c•
Mar 10, 2020
The external tool did not work. I believe there were some maintenance issues. Not good enough.
von Alessandra B•
May 10, 2020
Not engaging. Had problems opening the notebooks at the beginning of the course
von sylvain g•
Mar 19, 2020
A lot of mistake in the materials.And some labs exercise were unreachable.
von Alistair K•
Jun 11, 2020
Utterly abysmal! The lecturer is clearly reading from a script an never actual explains or discusses anything.
The monotonous tone is surely a ML synthesis?
All of the usual typos and code bugs, however even worse than is the fact that some key slides only stay on screen for less than 1 second. A very poor effort on the lecturer's part.
von Mohammad M A•
May 03, 2020
this has been the worst course I have ever seen... the guy is not able to explain as it seems the audience of his course are mathematicians... he makes explanations by showing things and saying numbers but without explaining the principles behind it...
von Łukasz C•
Mar 18, 2020
Overall good course and labs. But labs are so unstable, that it makes this course useless. Out of 4 weeks labs were not accesible for more than a week. Not recommended
von Pratik B•
Apr 12, 2020
Sorry to say, but I really had some high hopes from this course, but this course is not meant to be a part of any specialization.