This course was not to the same standard as some others I've taken on Coursera. I think the concepts would have been very hard to follow if I hadn't already taken the Deep Learning specialization, so it isn't a great conceptual introduction to Deep Learning. That said, it also doesn't deeply explore the nuances of the PyTorch library, or give very much guidance on best practices or how it differs from other popular frameworks like Keras/TensorFlow. The quiz questions are fairly shallow (and often frustratingly ambiguous). Probably the best part of the class are the ungraded lab assignments.

I like to feel a human is teaching me, but I felt a robot is teaching instead. Also, many parts of the labs are copied from each other, and that's good, but the sentences and comments are forgotten unchanged and they don't belong to that lab. I preferred if the PowerPoints were designed better. Other than that, I can see that the author tried his best to include everything.

The presenter at times goes too fast and once he's finished talking the slide moves forward before there is time to absorb the material. The slides also contain errors. Should be more throughly reviewed. The labs also contain some bugs. The quizes contain some spelling mistakes and some of the quiz questions are unclear.

I have just one thing to say: there is too much packed into a single course. Astonishingly, this course includes 44 notebooks, each of considerable length. I believe it would be more appropriate to split this single course into three or four courses and organize them as a single specialization.

Potentially a good course, but due to the very short videos and complete lack of supporting material (not even the slides of the videos), it's hard to follow. You need to watch the videos over and over again and take notes. Not sure why not even the lecture slides are provided.

The videos were informative, the Jupyter notebooks were also fine, however, when it came to using Watson Studio, there were way too many problems to deal with. Perhaps a more user-friendly approach for the final project/assessment would have been better.

Very theoretical course. You can claim the badge without running any code. The additional honours course consisted of a total of 3 lines of code you had to write. I did not really enjoy this course. It covered a lot of things but was as dry as possible.

It is very fast paced. There are a lot of videos and not enough opportunities to actually reinforce what you have learned in terms of shorts projects. I think at the end of each week, there should be a small project in order to progress.

The lectures and quizzes are too short to provide much value. The material could be better condensed. The labs were useful, although at times, it felt like the same material from a prior video. In general, it was a good overview.

Ok walkthrought of pytorch, a lot of content but slight mismatch between rather basic DS topics and advanced programming skills. Materials need to be reviewed for spelling and grammar, some quiz questions are unclear.

Overall a good introduction to Pytorch and machine learning. But, some of the questions in the assignments have the wrong answers and/or are worded in a confusing way (can be interpreted in different ways).

the content is definitely overloaded.. i am blowing.. felt like i went to college again. if cut some of the content it will be much more user friendly to learn.. for an online class prespective

The topics selected for the course are good, but the implementation is far from perfect. The quizzes seem to be generated by some machine learning algorithm. Expected better from IBM.

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.

The later labs had little or no practice exercises. The typos/mistakes in the materials were a distraction. It was disappointing to not have a fine tuning lab in the last week.

Ok to show you how to use pytorch, but to learn ML you should really take the famous course everyone is talking about, it's much better.

quality of slides is quite bad and exercises are just a repetition of the class. Nevertheless the basic concepts are explained clearly

good material but you should polish the voiceover and check for spelling/formatting mistakes - of which there are many

I got trouble with some exercises, also there are some grammar errors and the explanation in some videos are not good

Very slow and redundant material with previous courses of the "IBM AI Engineering Certificat Professionnel"

More useful exmples in labs would be helpful to understand the possibilities with the method and tool

quality of videos at the beginning of course are low, fells like the machine is reading it

To focus in the coding but not the underlying structure of the library and how to use it.

There should be slides available for downoad.

The pace of the course was too fast.