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Volver a Deep Neural Networks with PyTorch

Opiniones y comentarios de aprendices correspondientes a Deep Neural Networks with PyTorch por parte de Habilidades en redes de IBM

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Acerca del Curso

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...

Principales reseñas

SY

29 de abr. de 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

15 de may. de 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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251 - 275 de 283 revisiones para Deep Neural Networks with PyTorch

por Prateeth N

1 de jul. de 2020

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

por Bhaskar N S

4 de abr. de 2020

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

por Pakawat N

5 de may. de 2020

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

por Liam A

5 de feb. de 2021

OK for beginners, superficial exercises and quizzes.

por Suman S

3 de may. de 2020

The course is too heavy to have just one project.

por 谭皓博

15 de jul. de 2020

A number of mistakes were found in the course.

por Yuping Y

17 de abr. de 2022

It is basically learning by copying code

por Tanmay G

20 de feb. de 2022

Good course

por Qihan L

24 de ago. de 2022

This

por Ryo S

8 de ene. de 2023

If executed properly, this course should have been great, covering a good set of topics necessary to start working on DNN using PyTorch. Unfortunately, there are several issues that lowers the quality of this course.

Slides switch too fast after showing the last element, which is often an element or statement that is most important on the page. Too many typos on slides and notebooks. Labs often fail to launch due to a 500 error and when they do, notebooks often don't work due to outdated libraries. Most of the quizzes are too trivial that can be answered without understanding the important concepts covered in the videos (but some quiz answers are wrong as discussed in the forum; they are not fixed after years).

Personally I don't think it's worth $50 and so I've finished it in the first week before paying.

por Johannes D

10 de jun. de 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.

por Dan P

13 de may. de 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.

por Will G R

11 de feb. de 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.

por Iain G

2 de abr. de 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.

por Moritz A

13 de jun. de 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.

por Alex D

4 de oct. de 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.

por Victor B

27 de mar. de 2020

I found the course instruction is confusing, sequential and class module should be in different video parts

por Jack C

10 de mar. de 2020

The external tool did not work. I believe there were some maintenance issues. Not good enough.

por Nicolas B

20 de oct. de 2021

The course contents are not very interesting, and the quizzes are way too easy.

por Alessandra B

9 de may. de 2020

Not engaging. Had problems opening the notebooks at the beginning of the course

por sylvain g

19 de mar. de 2020

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

por Karthik R

9 de abr. de 2021

Quite a few errors and lacking flow in the explanations.

por Dennis T

31 de oct. de 2020

not indepth enough explaination

por Sharad J

29 de nov. de 2021

Very high level.

por Muzamal A

3 de may. de 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...