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Learner Reviews & Feedback for Deep Neural Networks with PyTorch by IBM

4.4
stars
567 ratings
122 reviews

About the Course

The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch's tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed by Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers. Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered. Learning Outcomes: After completing this course, learners will be able to: • explain and apply their knowledge of Deep Neural Networks and related machine learning methods • know how to use Python libraries such as PyTorch for Deep Learning applications • build Deep Neural Networks using PyTorch...

Top reviews

SY

Apr 30, 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 16, 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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51 - 75 of 123 Reviews for Deep Neural Networks with PyTorch

By Irfan S

May 31, 2020

Labs were detailed one.

By Samira G

May 30, 2020

Outstanding course...

By David S

Mar 29, 2020

Fantastic explanation

By Julien V

Jun 03, 2020

Great course !

By Aditya G P

Apr 28, 2020

Awesome course

By Marvin L

Feb 06, 2020

It was Good !!

By Dishit P

Apr 27, 2020

best course

By Branly F L

Apr 09, 2020

Nice..!!

By Krishna H

Apr 28, 2020

Good!

By Miele W

Feb 16, 2020

Well, as there are no sort of exams or real questions to answer in order to pass, it strictly depends on how much attention you put in following this course. IMHO if well studied, it gives you a solid foundation, in order to let you explore the pytorch module.

By Philippe G

Mar 10, 2020

Very interesting course. Gives a good introduction to pytorch. My only concern is the quality of the quizzes: It is often limited to 2 very simple questions. This does not allow you to validate that you had a good understanding of the said topic.

By Luca R

Mar 29, 2020

At the beginning, PyTorch framework seems very hard to understand. At the half of course you begin to have a clear vision of the problems. A negative point is the notebook for every topic. I would suggest one for week with everything inside.

By Eric

Jan 20, 2020

Good, thorough course. Does not hold the student to any kind of standard or accountability and quizzes are ridiculously easy to pass.

By Pietro D

Jan 03, 2020

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

By Paranjape A J

Feb 13, 2020

More graded coding assignments would have been better, but content is good!

By Marcin L

May 01, 2020

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.

By Konstantin S

Feb 24, 2020

Poorly prepared materials, awful quiz modules, lots of mistakes

By Oussama B

Feb 27, 2020

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

By sada n

Jan 10, 2020

it is too deep

By A A A

Jul 06, 2020

This course is really good in explaining the concepts and pytorch. However, I found the course too segmented. Some lectures, some quizzes, and some labs can be combined. Example for week 1, I think 1.1 (introduction to tensors), 1.2 (1d tensors) and 1.3 (2d tensors) can be combined to single lecture or all 3 lectures be one after another making it appear like it’s together. The 2 labs can be combined into a single notebook. The 2 quizzes can be combined into 1 quiz of maybe 5 or more questions. Similarly, 1.4 (Simple Datasets) and 1.5 (Datasets) can be combined, and so on. I also think that the honours content about batch normalization should be included as part of normal contents. Maybe more advanced concepts can be put up as honours contents.

By Erdem Ş

Jun 17, 2020

even with no mandatory peer graded assignment, for me it was the hardest course to learn in "IBM AI Engineering". So many topics and so many codes to check for each week. i liked it. i believe i will revisit the materials in the future.

By Okta F S

Jun 18, 2020

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

By Zhenzhou Z

Jul 01, 2020

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

By Ayush k

Jul 06, 2020

incredible course covering from basics to a satisfaction level

By Farhad A

Jun 16, 2020

It was well structured . Thank you