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

4.4
stars
912 ratings
212 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 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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201 - 212 of 212 Reviews for Deep Neural Networks with PyTorch

By sylvain g

Mar 19, 2020

A lot of mistake in the materials.And some labs exercise were unreachable.

By Hüseyin D K

Dec 27, 2020

Very short videos. Speaking so fast. It's like presenting not education.

By Karthik R

Apr 9, 2021

Quite a few errors and lacking flow in the explanations.

By Dennis T

Oct 31, 2020

not indepth enough explaination

By Ulrich S

Dec 7, 2020

The course is extremely slow and low level. Some important information on the slides might be around for less than a second, whereas unimportant information might be repeated several times. There is too many quite similar labs and very few background information. The practice parts and even the final assignment are way too simple.

At least, it seems one is able to learn some basic notion of PyTorch,

By Alistair K

Jun 11, 2020

Utterly abysmal! The lecturer is clearly reading from a script an never actual explains or discusses anything.

The monotonous tone is surely a ML synthesis?

All of the usual typos and code bugs, however even worse than is the fact that some key slides only stay on screen for less than 1 second. A very poor effort on the lecturer's part.

By Muzamal A

May 3, 2020

this has been the worst course I have ever seen... the guy is not able to explain as it seems the audience of his course are mathematicians... he makes explanations by showing things and saying numbers but without explaining the principles behind it...

By Łukasz C

Mar 18, 2020

Overall good course and labs. But labs are so unstable, that it makes this course useless. Out of 4 weeks labs were not accesible for more than a week. Not recommended

By Kartik S

Oct 26, 2020

the explanation is not in detail. Course Structure is confusing as well. Sometimes the concepts taught are not entirely correct. Overall not a good experience.

By Pratik B

Apr 12, 2020

Sorry to say, but I really had some high hopes from this course, but this course is not meant to be a part of any specialization.

By Walter c

Jun 20, 2021

The agenda is good but it is not well explained.

By Javier J M

Feb 22, 2021

Sucks!!