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Explainable AI: Scene Classification and GradCam Visualization (으)로 돌아가기

Coursera Project Network의 Explainable AI: Scene Classification and GradCam Visualization 학습자 리뷰 및 피드백

4.7
별점
46개의 평가
8개의 리뷰

강좌 소개

In this 2 hour long hands-on project, we will train a deep learning model to predict the type of scenery in images. In addition, we are going to use a technique known as Grad-Cam to help explain how AI models think. This project could be practically used for detecting the type of scenery from the satellite images....

최상위 리뷰

필터링 기준:

Explainable AI: Scene Classification and GradCam Visualization 의 8개 리뷰 중 1~8

교육 기관: Vipul G

2020년 7월 27일

I like the course, it is exceptional.

But if you provide the materials(train/test files) to download it will be better to apply it on our own

교육 기관: Alexandros O

2020년 12월 25일

(+) Very insightful introductory project course to CNN and XAI. The instructor was explaining as much as possible to all parts. Providing such images was really helpful.

(-) There were several mistakes in the code. A prerequisite for this course could also be the mathematical background and thus, more explanation on why and how each mentioned-part could be provided. Not all explanation parts for XAI are provided to jpnb for students.

교육 기관: Yaron K

2021년 9월 26일

A step by step explanation of how to build a Resnet Image Classification Convolutional Neural Network. Including how to use a technique known as Grad-Cam to visualize how different parts of the image effect the final classification.

Cons: No theory. It shows all the pieces of a working model. But not WHY it works.

Note: the notebook in Files is empty. The mostly complete notebook is in Files-->Notebooks

교육 기관: Jesus M Z F

2020년 7월 19일

Excelente curso, Muchas gracias

교육 기관: Stud 2

2020년 8월 1일

very helpful

교육 기관: Kamlesh C

2020년 7월 27일

thanks

교육 기관: Samy S S E

2020년 8월 26일

it's an exciting course it covers all machine learning life cycle steps in a short time and organizable way

교육 기관: Simon S R

2020년 9월 2일

This project should be more about GradCam Visualization and should dive deeper into its details, but not provide an explicit overview of all the steps necessary to build the original model.