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

Content wise this is very good for beginners, who have basic Numpy, Python, DL understanding. Only issue would be the automated voice of the instructor. That can be changed to make it more human friendly!

The material is good. I found the assignments a bit too easy. A bit more challenge would be welcome. I found the artificial voice with the lectures to be distracting. The AI isn't quite good enough.

I learned loads in this course. I'm quite familiar with Keras so it was good to use a different package. The instruction was very clear but LONG. I would have liked the labs to have been more involved.

A nice landing on Pytorch and basic Deep Learning concepts. I liked the collection of code and practical examples. If only, I missed having more difficult practical assignments along the course.

Excellent course, works its way through basics to fully fledged machine learning models at a good pace. A few of the examples used in the lab code throw errors, these should be rectified

Course material is great, although it has some errors, as on the video slides as in the notebooks. This should be rectified. Also, the assessments and quizzes should definitely be harder.

Pros: The course is extremely well structured. The presentations are very informative and clear also well explained.

Cons: The assignments and quizzes are not challenging at all

Good to dive into Deep Learning and get some PyTorch basics. However, there're sometimes mistakes in the assignments. Also, the explanations can sometimes be a bit confusing.

Amazing course for a beginner in Deep Learning & Pytorch.

I gave 4 stars as I expected it to be more pytorch heavy.

Overall, a really good crafted course.

Very intensive course. Could do more training labs. But this is definitely a very dense course. Extremely helpful to get started on ML/Deep Learning.

Good introduction of PyTorch. There are some minor code errors and inconsistencies in the material but generally not difficult to figure it out.

Good PyTorch training course with clear structure and content in general, except that there are some small bugs in the labs.

The courses provides basic knowledge, but I wish that it was a bit more advanced and had more challenging assignments.