Grad-CAM Explained | FREE XAI Course | L7 - Gradient-weighted Class Activation Mapping

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Gradient-weighted Class Activation Mapping (Grad-CAM) is an explainable AI (XAI) method that highlights which parts of an image influenced an AI model’s decision. By generating a heatmap, it reveals how convolutional neural networks (CNNs) "see" images, making deep learning models used for medical diagnostics, autonomous driving, and image recognition more transparent and interpretable.

In this lesson, we’ll break down:
✅ The theory behind Grad-CAM
✅ Visualizations and the math behind the method
✅ Intuition on why CAM-based approaches work
✅ Advantages & limitations of Grad-CAM

🚀 Useful playlists 🚀
XAI for CV:    • XAI for CV  
XAI:    • Explainable AI (XAI)  
SHAP:    • SHAP  
Algorithm fairness:    • Algorithm Fairness  

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🚀 Chapters 🚀
00:00 Introduction
02:07 Theory
06:47 Maths formula
08:08 Intuition
10:36 Advantages and limitations