Great course !

Awesome course

It was Good !!

very helpful

best course

Nice..!!

It is a nice course to get you into Pytorch and with some insightful views of how some ML algorithms work but adding to the most upvoted review, the synth voice dialogue sometimes doesn't make sense, the inflections on the speech are weird at times, it spells things that come from a text based explanation rather than someone speaking (things like spelling "I E for -for example- and C N N for convolutional neural network among many, many others)... sometimes the voice is talking about one thing and something else is highlighted on the video, time mismatch...

Many grammar mistakes, stuff left in the examples and quizes that doesn't make sense... definitely needs a redaction and content check.

Practice sessions are organized in a tool that doesn't have enough computing power for training neural networks. The networks often take hours to train and you have to constantly monitor them because if you don't, the tool will automatically sign you out and you will lose your results.

I also don't like the mechanistic reading style (sounds like a bot reading), lack of human interaction doesn't seem to work for lectures.

This course had many flaws including that at the most basic it was riddled with errors, typos, and formatting issues.

Some more specific feedback is that this course seemed overly preoccupied with explaining math concepts or neural net architecture at a high level and glossing over much of the actual pyTorch specific programming.

The organization of the lectures make no sense, with separate lectures and labs for single class and multiclass versions of various models even though the functions all were built to handle multiple dimensions and so there was really no difference. Additionally because the lectures, lab, and quiz used all the same examples this means we would see the exact material presented over and over with no clear pedagogical reason.

Additionally the course seemed overly preoccupied with OOP to the point of replicating the functionality of several built in pyTorch classes obfuscating the actual material with no clear reason given for why we were creating our own version of extant classes.

Lastly, the quizes almost never asked any questions about pyTorch. Most of them were just the most basic questions about comprehending reading code. Things like "if input = 3 how many inputs are there?" or "which option is used for He initialization" and the options are like "He initialization or Xavier"

Very good course only , complete the practical assignments they are important.

As for exam the question answers should be based on practical outputs, say make a model for

this dataset or so. Paste the result for the score.

Fantastic explanation

I appreciate this course. Its really amazing course and if you are a beginner in Deep Learning and want to use and learn Pytorch then this course is really good to start.

One thing about this course is that some important topics like RNN, R-CNN , text and sentiment analysis, time series are not included in this course which I think should be included.

The course is interesting and well organized but the quiz are not challenging and full of typos.

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.

Poorly prepared materials, awful quiz modules, lots of mistakes

I am very disappointed with the quality of the course materials. The videos are recorded with what sounds like a text to speech system or a voice over done by a voice actor who does not really understand the subject matter and lacks personality.

It's hard to understand as it all runs at the same pace and there isn't sufficient time given to specific concepts that may take a shorter or a longer time to sink in depending on their complexity. It's just a constant speed monologue without any real feeling or passion in the subject matter.

Bad !!!!! Many mistakes, questions too easy !!! I am really disapointed

The right level of detail so that you can dive in.

I wish there had been a week to cover RNNs as well though, in particular the best way to handle variable length sequences for RNNs :)

Wonderful course!!! Best among all the courses under AI Engineer Certificate by IBM. Deep learning always haunted me with the maths involved but now I get a very good start with this.

Excellent Course. I love the way the course was presented. There were a lot of practical and visual examples explaining each module. It is highly recommended!

By this course I can understand the basic concept for building neural network or deep lerning model using PyTorch. Very Good course to beginner.

It would be better to add a section explaining the experiment code of the famous paper.

Quiz questions are very easy. Graded Programming Assignments would have been better.

clear instruction, great ilustration and process description. Thank you so much

incredible course covering from basics to a satisfaction level