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Deep Neural Networks with PyTorch(으)로 돌아가기

IBM 기술 네트워크의 Deep Neural Networks with PyTorch 학습자 리뷰 및 피드백

1,161개의 평가

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 258개 리뷰 중 1~25

교육 기관: Janis S

2020년 3월 19일

In general, this course is very useful and I have learned a lot.


A large amount of information has been compressed in a short period of time. The synthesized speech runs too fast. Slides on videos also change too fast. Some of them appear less than a second. A learner has to pause or even rewind a video to catch and explore them for understanding.

Multiple errors in lab solutions (I would call them even bugs). Some of the proposed solutions do not follow requirements specified in preceding cells.

Wrong information in slides. For example, 'hidden layers' instead of 'neurons in the hidden layer' or 'hidden neurons'.

Too many spelling errors in videos, quizzes and descriptions apparently made by non-native English speakers. For example, two instead of too, ture instead of true, supper instead of super, rergresstion instead of regression etc.

Technical issues with labs appear too often - cannot start the server or unavailable at all for multiple days, broken conda installation due to outdated or incompatible module versions (in particular, torchvision and pillow).

I was expecting much more accuracy from a course led by IBM. An editor would be recommended to thoroughly review all the slides, quizzes and notebooks of this course.

교육 기관: Michael X

2020년 1월 7일

Still a decent course but compared to other courses in this series, both the content and the

presentation of the content really lack clarity.

교육 기관: Prosenjit D

2019년 12월 26일

Horrible slides, instructor's monotonous voice, typos in exercises, and explanations are inadequate. Course is a rip off at 50 dollar a month.

교육 기관: Robson A

2020년 5월 16일

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.

교육 기관: Henrik S

2019년 12월 10일

While the subject of this course is interesting, the general quality of the course materials is sub-standard of what I am used to on Coursera. I posted a question on the forum that the staff never bothered to answer. I used to a much better quality from Coursera.

교육 기관: Jordan W

2019년 12월 17일

A terrific overview of PyTorch. I was especially amazed by the lab notebooks where the author went above and beyond to plot everything in a useful way. This allowed the student to visualize everything that was going on under the hood. In each notebook, there was also multiple ways of showing how to accomplish a task whether it be coding manually or using a PyTorch function to simplify. I appreciate seeing it both ways as it really demystifies the black box of Deep Learning libraries.

교육 기관: Jeremiah J

2020년 2월 20일

It was a LONG course, very packed with info. But, I feel like I certainly learned a lot and have a great foundation for further learning.

교육 기관: lorenzo a

2020년 4월 3일

The main reasons i gave 5 starts:

1 There is simply a lot of content in this course

2 You can tell that the explanations were thought through and that reflects in the quality of the content

3 The labs are super helpful and presented in a very understandable way

Sure the course doens't cover some topics such as recursive neural networks, but you won't be disappointed unless you are looking for a very very technical course on NNs

교육 기관: Ravi P B

2020년 5월 17일

A very excellent course to get introduced to PyTorch from bottom up.Also the lectures for Neural Networks and CNNs were short but really excellent and highly intuitive.These short lectures are an excellent way to learn concepts of Neural networks.Would have loved to see a week dedicated to sequence models.The instructors have really really done a fantastic job.

교육 기관: Yong S

2020년 2월 3일

Very well done course! The concepts are pretty clearly explained. Sometimes the labs have instructions that are a bit misleading but it's a very minor issue. I really enjoyed the instructor using colored blocks as a tool to explain codes!

교육 기관: shanmukha y

2020년 4월 30일

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!!

교육 기관: Aïssatou N

2020년 3월 7일

It was a very informative and interesting lecture. I learn a lot about the details when using PyTorch to build and train a deep neural network. I am so thankful.

교육 기관: kenneth W

2020년 5월 9일

Excellent course is full of great information. A bit long. Since this is part of a series, some of the information is a bit repetitive.

교육 기관: Daniel K

2019년 11월 20일

Amazing, really informative and helps a lot !!! really liked this course and would recommend this to anyone interested in Deep learning!

교육 기관: Arct J Z

2020년 3월 25일

It is freeaaakin hard if you take the whole IBM AI ENGINEERING Professional Cert in the duration of a trial period.

교육 기관: Ankush K

2020년 4월 30일

Very good course only , complete the practical assignments they are important.

As for exam the question answers should be based on practical outputs, say make a model for

this dataset or so. Paste the result for the score.

교육 기관: Cristina A G

2020년 2월 20일

One of the best courses I've taken. Everything was really easy explained, step-by-step, with nice slides and lost of explanations. It is really clear and starts from the very beginning. I'll totally recommend it!

교육 기관: Mohamed E

2020년 3월 30일

this course provides a very good and cohesive introduction to Neural Networks. I learned a lot during my journey and I recommend it for anyone interesting in the field.

교육 기관: Shinhoo K

2019년 11월 16일

Awesome! This course gives me the basic workflow for using machine learning technique in my research! The materials in the form of Jupyter lab really help!

교육 기관: Bằng P C

2020년 2월 17일

A good course for people who want to start with pytorch framework. This course start from sample problem to an complex ones help people understand easily.

교육 기관: Nishant P

2020년 5월 16일

A very thorough introduction to Pytorch. The course is general enough to learn other frameworks like Tensorflow and Keras as well.

교육 기관: Sreena R

2020년 5월 21일

Thanks a lot Mr.Joseph Santarcangelo for the wonderful sessions. I could follow all lessons. It was extremelly helpful

교육 기관: Daniel J B O

2020년 5월 26일

Very complete and challenging course for intermediate level in the creation and applications of neural networks

교육 기관: Theodore G

2020년 5월 16일

Great course packed with loads of learning. The labs helped reinforce the content of the videos. Thanks!

교육 기관: Konrad B

2020년 2월 1일

Excellent Course. The Instructor put a lot of work into the content. Thank you for sharing the knowledge