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学生对 Coursera Project Network 提供的 Explainable AI: Scene Classification and GradCam Visualization 的评价和反馈

4.7
46 个评分
8 条评论

课程概述

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....

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1 - Explainable AI: Scene Classification and GradCam Visualization 的 8 个评论(共 8 个)

创建者 Vipul G

Jul 27, 2020

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

创建者 Alexandros O

Dec 25, 2020

(+) 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.

创建者 Yaron K

Sep 26, 2021

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

创建者 Jesus M Z F

Jul 19, 2020

Excelente curso, Muchas gracias

创建者 Stud 2

Aug 1, 2020

very helpful

创建者 Kamlesh C

Jul 27, 2020

thanks

创建者 Samy S S E

Aug 26, 2020

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

创建者 Simon S R

Sep 2, 2020

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.