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Deep Neural Networks with PyTorch(으)로 돌아가기

IBM의 Deep Neural Networks with PyTorch 학습자 리뷰 및 피드백

4.4
stars
132개의 평가
16개의 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

SK

Nov 17, 2019

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!

RR

Dec 09, 2019

Very Clear explanation and rich labs. The quiz can be more challenging

필터링 기준:

Deep Neural Networks with PyTorch의 16개 리뷰 중 1~16

교육 기관: Jordan W

Dec 17, 2019

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: Daniel K

Nov 20, 2019

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

교육 기관: Henrik S

Dec 10, 2019

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: Prosenjit D

Dec 26, 2019

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: Shinhoo K

Nov 17, 2019

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: RuoxinLi

Dec 09, 2019

Very Clear explanation and rich labs. The quiz can be more challenging

교육 기관: Vittorino M

Dec 09, 2019

Aprendí muchísimo. Gracias.

교육 기관: Pavan D

Nov 19, 2019

very intuitive and in depth

교육 기관: Farrukh N A

Dec 09, 2019

Best course on AI

교육 기관: ThanhTung

Dec 25, 2019

very helpful

교육 기관: Theodore G

Jan 11, 2020

Very intensive course. Could do more training labs. But this is definitely a very dense course. Extremely helpful to get started on ML/Deep Learning.

교육 기관: Pietro D

Jan 03, 2020

The course is interesting and well organized but the quiz are not challenging and full of typos.

교육 기관: Krishna S B

Dec 27, 2019

It would have been better if graded programming assignments were there.

교육 기관: Youness E M

Dec 21, 2019

There is a number of errors in the courses and in quiz

교육 기관: Michael X

Jan 07, 2020

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: sada n

Jan 10, 2020

it is too deep