awesome course

V good course

nice content

Great Course

it was good

tres bien

Excelente

nice

Good

good

nice

The course is good and has a nice mixture of theory and practice, which is essential for mastering complex concepts. However, I do have a few observations about the course quality:

- Several of the slides in the presentations and even the labs have a lot of grammar mistakes.

- The theory is often rushed in the lectures. The course would greatly benefit from a more careful analysis of the maths behind each concept.

-In its effort to make the concepts easier to grasp, the lectures keep using coloured boxes to replace mathematical terms. I found that to be more confusing, they use far too many colours and are too liberal with their use.

-Lastly, the labs completely broke down in the second half of the course. My understanding from the course staff is that an upgrade was made on the backend which did not go well and thus caused those issues. They should have several backup plans for those occurrences, starting with having the labs available for download so that the students can do them offline.

Overall I'm happy with the course and would cautiously recommend it, given the above shortcomings.

The course was fantastic for someone like me. I already knew all the math, and the course gave deep exposure to the needed Python routines and classes. The labs really help cement the knowledge.

Only drawback is that it went a bit too slow for me (NN with one input, NN with two inputs, NN with one output, NN with two outputs, etc.), but others might disagree.

I'm giving it a four because there were so many typos and mistakes (i.e. the gradient is perpendicular to countour lines, not parallel), lots of mispellings and wrong data on the slides and the speaker sounded like a computer (he pronounced the variable idx as "one-dx" - huh? I understand that there's going to be mistakes, but this is an one online course made for many people, and you'd expect that kind of stuff to be corrected over time since it is being repeatedly delivered.

But - it was a great course and I highly recommend taking it.

The content and its aplications are both amazing! Nonetheless it REALLY need some review! There are A LOT of grammar mistakes (missing characters, merged words, and so on), sometimes what is said on the videos is different from shown on the screen, there are lots os wrong questions on the quizes (the same question can be shown up two times with the exact same text and with two different answers each time). Some labs are missing code blocks ("Practice" parts with just the answer but it is not written what are we suposed to do... for example). I would like to thank IBM for the content, but it needs some improvement and THERE ARE LEARNERS COMPLAINING ABOUT THIS SAME ISSUE FROM YEARS AGO!

Despite the irritating computer voice and sloppy slides it is a good course. It is less a PyTorch course but an very nice introduction into ML and deep learning in general. Important concepts are introduced without overboarding the material with too much Math.

The labs could be more interesting and challenging. Towards the end the IBM Cloud was not working any more, before it was really convienent to do the labs in the browser. However, there are only a few requirements and anyone with a little Python experience can quickly setup a virtual environment. However, an instruction and a requirements.txt would be nice.

The topics and information were just right for my purposes. Unfortunately, the execution was a bit off. Misspellings everywhere, lab software (IBM Watson Studio) that didn't work, so that I had to download the labs and run them on my own machine, and labs with incomplete explanations, by which I mean only code cells toward the end. However, looking past that, this course is valuable. I left with a good understanding of PyTorch, and now I feel ready to dive into the PyTorch documentation.

Here is a list of pros and cons:

Pros: great notebooks and many examples

Cons: the videos are a bit "cheap" (typos and artificial voice) and often miss the intuitions ("To do that, we code like this"). A bit light on the maths. Quizzes are too easy to validate (people may validate with a superficial understanding of what is going on).

Summary: The value of this class resides in the notebooks and in the time your are willing to invest in them.

I think it's a good course if you're coming in with the notion of deep learning pretty much clear and are more interested in learning the PyTorch syntax. I'm not sure how useful the course would be in terms of learning ML or DL from scratch. In particular the conceptual slides could be better.

The notebooks are well-prepared. Even though occasional bugs can be found, they aren't much to worry about.

The course gave a decent and well-structured introduction to PyTorch. However, I would have hoped for less typos (including in the code on the slides), more challenging and instructive quizzes and real exercises (there are instructive labs, but the practice section is usually only a very slight modification of the already given code).

Awesome course for learning the basics/fundamentals of Pytorch. However the labs often would not run some of the more complex or CPU-intensive models, so I would suggest downloading the labs to your local machine. Also could have also used more assignments for hands-on experience, but I would recommend this course.

Exceptional course. The lectures were little monotonous and robotic, I like this courses to be instructed by human speakers, but this did not affect the content of this course, the clarity on the topics and how well it was explained, it helped also to improve my knowledge on computer vision.

Great course.

Very good fundamental course.

It will be good if real data is used in lab rather than using virtual data.

Also, the notebook's hyperlink of the final assignment isn't work. I can't import the notebook to Watson studio and finish the assignment, please fix it, thank you.

Explanation was confusing as time, and text-to-speech lecturer made it harder to engage. Lots of typos and unintuitive phrasing. However, taught useful skills, and all the resources were there to do own thinking/research and eventually understand everything.

Concepts presented in nice bite size chunks. Labs help reinforce concepts. BUT, felt like course was just a bunch of pieces with little assembly. Kinda like finding a box of LEGOs (r) with nothing to really build from them.

Good explanation with examples of code in python. The concept of convolution can be elaborated upon further as to it's genesis and how multiple processing techniques such as max pooling impact performance