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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 IBM

4.4
estrellas
1,024 calificaciones
226 reseña

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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126 - 150 de 227 revisiones para Deep Neural Networks with PyTorch

por Sourabh K

26 de jun. de 2021

One of the best course in the IBM AI Engineer Specialization !

por Emanuel N

13 de feb. de 2021

Gran curso super detallista y explica muy bien los conceptos

por Milad E N

19 de dic. de 2020

it goes through neural network and builds it from scratch.

por Huy P

5 de mar. de 2021

This course is basic and so foundational for begining

por Hasan G

21 de ago. de 2020

I have learned good skills for deep neural networks

por 林靖翰

30 de jul. de 2021

The teaching of this course is clear and complete

por Krittamet K

23 de may. de 2021

Much more understand how deep neural works!!

por Luis C

17 de ago. de 2020

best introduction course on the subject.

por ilovecats

29 de abr. de 2021

awesome, this is like 2 courses in one

por Arijit B

28 de feb. de 2021

An excellent introduction to PyTorch.

por Oscar A C B

10 de jun. de 2020

Excellent! Just what I needed.

por Abdoulaye B K

6 de nov. de 2020

The content was on point.

por Lixy

10 de ago. de 2021

easy to understand

por Amir J

12 de ago. de 2020

Amazing course!

por arash h

30 de nov. de 2021

perfect course

por CHALLA K S N M S

21 de sep. de 2020

awesome course

por Aditya M P

8 de dic. de 2020

Good Course

por Godwin M

26 de sep. de 2021

AWESOME

por 徐淇

3 de ago. de 2021

good!

por Abdullaev S

6 de mar. de 2021

Coll!

por ASITHA I D

15 de feb. de 2021

Good.

por Marco C

30 de mar. de 2020

The course is good and has a nice mixture of theory and practice, which is essential for mastering complex concepts. However, I do have a few observations about the course quality:

- Several of the slides in the presentations and even the labs have a lot of grammar mistakes.

- The theory is often rushed in the lectures. The course would greatly benefit from a more careful analysis of the maths behind each concept.

-In its effort to make the concepts easier to grasp, the lectures keep using coloured boxes to replace mathematical terms. I found that to be more confusing, they use far too many colours and are too liberal with their use.

-Lastly, the labs completely broke down in the second half of the course. My understanding from the course staff is that an upgrade was made on the backend which did not go well and thus caused those issues. They should have several backup plans for those occurrences, starting with having the labs available for download so that the students can do them offline.

Overall I'm happy with the course and would cautiously recommend it, given the above shortcomings.

por Peter P

8 de jul. de 2020

The course was fantastic for someone like me. I already knew all the math, and the course gave deep exposure to the needed Python routines and classes. The labs really help cement the knowledge.

Only drawback is that it went a bit too slow for me (NN with one input, NN with two inputs, NN with one output, NN with two outputs, etc.), but others might disagree.

I'm giving it a four because there were so many typos and mistakes (i.e. the gradient is perpendicular to countour lines, not parallel), lots of mispellings and wrong data on the slides and the speaker sounded like a computer (he pronounced the variable idx as "one-dx" - huh? I understand that there's going to be mistakes, but this is an one online course made for many people, and you'd expect that kind of stuff to be corrected over time since it is being repeatedly delivered.

But - it was a great course and I highly recommend taking it.

por Julien P

11 de jun. de 2020

Here is a list of pros and cons:

Pros: great notebooks and many examples

Cons: the videos are a bit "cheap" (typos and artificial voice) and often miss the intuitions ("To do that, we code like this"). A bit light on the maths. Quizzes are too easy to validate (people may validate with a superficial understanding of what is going on).

Summary: The value of this class resides in the notebooks and in the time your are willing to invest in them.

por Farhad M

24 de jun. de 2020

I think it's a good course if you're coming in with the notion of deep learning pretty much clear and are more interested in learning the PyTorch syntax. I'm not sure how useful the course would be in terms of learning ML or DL from scratch. In particular the conceptual slides could be better.

The notebooks are well-prepared. Even though occasional bugs can be found, they aren't much to worry about.