Convolutional neural networks (CNNs) have greatly improved tasks like image and sound classification, object detection, and regression-based analysis. Yet, these models often behave as "black boxes," producing accurate but difficult-to-interpret results. For engineers and developers, understanding why a model made a specific decision is critical to identifying biases, debugging errors, and building trust in the system.
This is a companion discussion topic for the original entry at https://www.edgeimpulse.com/blog/ai-explainability-with-grad-cam-visualizing-neural-network-decisions