Guided Backpropagation theory | FREE Explainable AI (XAI) Course with Python

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Convolutional Neural Networks (CNNs) rely on complex feature hierarchies to make predictions, but how do we see what they’re really focusing on? In this video, we break down Guided Backpropagation, a powerful technique for interpreting deep learning models. Unlike occlusion, SHAP, or Grad-CAM, which highlight important pixel regions, Guided Backpropagation reveals specific features contributing to a model’s output by modifying the backpropagation process with ReLU masking.

Whether you're diving into explainable AI (XAI), working on machine learning model interpretation, or just curious about visualizing neural network features, this video gives you the theory and intuition behind why Guided Backprop works so well.

🚀 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
01:25 Some terminology
02:17 Theory
04:07 ReLU masking
7:52 Intuition