Back to Deep Learning with PyTorch : GradCAM
Learner Reviews & Feedback for Deep Learning with PyTorch : GradCAM by Coursera Project Network
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About the Course
Gradient-weighted Class Activation Mapping (Grad-CAM), uses the class-specific gradient information flowing into the final convolutional layer of
a CNN to produce a coarse localization map of the important regions in the image. In this 2-hour long project-based course, you will implement
GradCAM on simple classification dataset. You will write a custom dataset class for Image-Classification dataset. Thereafter, you will create
custom CNN architecture. Moreover, you are going to create train function and evaluator function which will be helpful to write the training
loop. After, saving the best model, you will write GradCAM function which return the heatmap of localization map of a given c...
a CNN to produce a coarse localization map of the important regions in the image. In this 2-hour long project-based course, you will implement
GradCAM on simple classification dataset. You will write a custom dataset class for Image-Classification dataset. Thereafter, you will create
custom CNN architecture. Moreover, you are going to create train function and evaluator function which will be helpful to write the training
loop. After, saving the best model, you will write GradCAM function which return the heatmap of localization map of a given c...
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1 - 2 of 2 Reviews for Deep Learning with PyTorch : GradCAM
By Sayantan S
•Feb 21, 2023
Great material, easy to follow and to some extent helps build intuition.
By Harith Y
•Jan 11, 2025
Please explain more in detail and also cover some important prerequisities.