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Deep Neural Networks with PyTorch(으)로 돌아가기

IBM의 Deep Neural Networks with PyTorch 학습자 리뷰 및 피드백

1,022개의 평가
226개의 리뷰

강좌 소개

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...

최상위 리뷰

2020년 4월 29일

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!!

2020년 5월 15일

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.

필터링 기준:

Deep Neural Networks with PyTorch의 227개 리뷰 중 126~150

교육 기관: Sourabh K

2021년 6월 26일

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

교육 기관: Emanuel N

2021년 2월 13일

Gran curso super detallista y explica muy bien los conceptos

교육 기관: Milad E N

2020년 12월 19일

it goes through neural network and builds it from scratch.

교육 기관: Huy P

2021년 3월 5일

This course is basic and so foundational for begining

교육 기관: Hasan G

2020년 8월 21일

I have learned good skills for deep neural networks

교육 기관: 林靖翰

2021년 7월 30일

The teaching of this course is clear and complete

교육 기관: Krittamet K

2021년 5월 23일

Much more understand how deep neural works!!

교육 기관: Luis C

2020년 8월 17일

best introduction course on the subject.

교육 기관: ilovecats

2021년 4월 29일

awesome, this is like 2 courses in one

교육 기관: Arijit B

2021년 2월 28일

An excellent introduction to PyTorch.

교육 기관: Oscar A C B

2020년 6월 10일

Excellent! Just what I needed.

교육 기관: Abdoulaye B K

2020년 11월 6일

The content was on point.

교육 기관: Lixy

2021년 8월 10일

easy to understand

교육 기관: Amir J

2020년 8월 12일

Amazing course!

교육 기관: arash h

2021년 11월 30일

perfect course

교육 기관: CHALLA K S N M S

2020년 9월 21일

awesome course

교육 기관: Aditya M P

2020년 12월 8일

Good Course

교육 기관: Godwin M

2021년 9월 26일


교육 기관: 徐淇

2021년 8월 3일


교육 기관: Abdullaev S

2021년 3월 6일


교육 기관: ASITHA I D

2021년 2월 15일


교육 기관: Marco C

2020년 3월 30일

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.

교육 기관: Peter P

2020년 7월 8일

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.

교육 기관: Julien P

2020년 6월 11일

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.

교육 기관: Farhad M

2020년 6월 24일

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.