Visualizing CNN Predictions with Class Activation Maps

Deep learning models are often criticized for being black boxes, making it difficult to trust their decisions. Class Activation Maps solve this by highlighting the exact regions of an image that influence a convolutional neural network's classification. In this text-based course, you will learn how to implement and interpret Class Activation Maps to bring transparency to your computer vision projects. You will transition from simply training models to explaining their reasoning, using standard architectures like MobileNet-V2. What you'll learn: Understand the fundamental concepts of Explainable AI and why model interpretability matters in computer vision; Identify how convolutional neural networks process spatial information and retain feature localization; Implement standard Class Activation Maps to visualize model focus areas; Explore modern variations like Grad-CAM for broader compatibility with different network architectures; Apply pre-trained models such as MobileNet-V2 on ImageNet-1K data to generate real-world activation maps; Evaluate the reliability and limitations of visual explanations in deep learning. This course begins with core definitions of neural network interpretability before guiding you through the step-by-step logic of extracting feature maps and gradients. You will read clear explanations and study clean code implementations to build a solid foundation in explainable deep learning. This course is designed for aspiring data scientists, machine learning beginners, and computer vision enthusiasts who want to understand how deep learning models make decisions. Basic familiarity with Python is helpful, but no prior experience with explainability tools is required. Start reading today to make your computer vision models transparent and interpretable.