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Bewertung und Feedback des Lernenden für Explainable AI: Scene Classification and GradCam Visualization von Coursera Project Network

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8 Bewertungen

Über den Kurs

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 - 8 von 8 Bewertungen für Explainable AI: Scene Classification and GradCam Visualization

von Vipul G

27. Juli 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

von Alexandros O

25. Dez. 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.

von Yaron K

26. Sep. 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

von Jesus M Z F

19. Juli 2020

Excelente curso, Muchas gracias

von Stud 2

1. Aug. 2020

very helpful

von Kamlesh C

27. Juli 2020

thanks

von Samy S S E

26. Aug. 2020

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

von Simon S R

2. Sep. 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.