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Learner Reviews & Feedback for Introduction to Neural Networks and PyTorch by IBM

4.4
stars
1,719 ratings

About the Course

PyTorch is one of the top 10 highest paid skills in tech (Indeed). As the use of PyTorch for neural networks rockets, professionals with PyTorch skills are in high demand. This course is ideal for AI engineers looking to gain job-ready skills in PyTorch that will catch the eye of an employer. AI developers use PyTorch to design, train, and optimize neural networks to enable computers to perform tasks such as image recognition, natural language processing, and predictive analytics. During this course, you’ll learn about 2-D Tensors and derivatives in PyTorch. You’ll look at linear regression prediction and training and calculate loss using PyTorch. You’ll explore batch processing techniques for efficient model training, model parameters, calculating cost, and performing gradient descent in PyTorch. Plus, you’ll look at linear classifiers and logistic regression. Throughout, you’ll apply your new skills in hands-on labs, and at the end, you’ll complete a project you can talk about in interviews. If you’re an aspiring AI engineer with basic knowledge of Python and mathematical concepts, who wants to get hands-on with PyTorch, enroll today and get set to power your AI career forward!...

Top reviews

SY

Apr 29, 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!!

RA

May 15, 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.

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326 - 350 of 373 Reviews for Introduction to Neural Networks and PyTorch

By Tony D

Sep 8, 2020

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

By Mutlu O

Aug 4, 2020

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

By Miroslav T

Jun 8, 2020

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

By Benhur O J

Jan 30, 2020

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

By Chris R

Jul 23, 2022

There should be slides available for downoad.

The pace of the course was too fast.

By Stephen L

Jun 1, 2023

The start is good, but in the last 2 week, many things are not clearly explained

By Prateeth N

Jul 1, 2020

Very Basic course. Would have enjoyed more interesting examples in the notebooks

By Bhaskar N S

Apr 4, 2020

Found it very difficult to follow some of the content and assignments

By Pakawat N

May 5, 2020

There are a lot of mistakes in the slides and video but no updates

By Liam A

Feb 5, 2021

OK for beginners, superficial exercises and quizzes.

By Suman S

May 3, 2020

The course is too heavy to have just one project.

By 谭皓博

Jul 15, 2020

A number of mistakes were found in the course.

By Yuping Y

Apr 17, 2022

It is basically learning by copying code

By Hugues L

Jul 3, 2024

Too theoritical

By Tanmay G

Feb 20, 2022

Good course

By Qihan L

Aug 24, 2022

This

By Johannes D

Jun 10, 2022

More "Beginner" than "Intermediate"

From the title i expected a course introducing the depthos of PyTorch for (intermediate) Data Scientists. Instead the course is a shallow introduction to feed forward and convolutional neural networks with a little bit of PyTorch. The course targets beginners who want to learn the basics of ANN / CNN models and learn their first deep learning framework. All others should search for another course.

By Marc J

Mar 23, 2024

There is some good information and explanations in weeks 5 and 6. Overall the course is very unstructured and most of the big "Whys" are not clarified. In addition some basic rules of teaching should be learned by the instructors and applied to design this course. If you should do it in the labs you MUST talk about it in the videos! People payed for these courses so they MUST NOT be forced to learn contents autoditactically!

By Dan P

May 13, 2022

This course is an OK introduction to Pytorch and neural networks for beginners, but many better such introductions exist. fast.ai would be my first recommendation. There are numerous spelling errors, some of which seriously affect the correctness of statements.

The quizzes only test trivial knowledge, and don't go into any real depth.

The total content of the course is maybe 4 hours.

By Will G R

Feb 11, 2021

Material is good but riddled with grammatical errors and random typos that only make learning more difficult. Also topics are covered at a very minor depth and I often had to look through many additional resources to understand each topic presented.

By Jaeoh S

Apr 29, 2023

The material is fine but the presentation is not good.

-Often the lecturer shows not so important slides a few seconds but important slides only 0.5sec.

-So much about follow the code than really explaining what's going on.

By Iain G

Apr 2, 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.

By Moritz A

Jun 13, 2022

When doing IBM AI Engineering Course, I will hear content twice here. Explanations could be better. No graded Assignment. Only Quizes with 2-3 question with 2-3 choices.

By Alex D

Oct 4, 2020

Very technical and math-oriented. Even after completing it, I have no idea how to apply it to the real world. Seems everything is read using a computer voice.

By Jay C L

Apr 28, 2024

There are too many typos in the notebooks and quizzes, even wrong indices of materials, including videos and notebooks.