Explainable AI: Saliency Maps and Integrated Gradients

Deep neural networks are often criticized as black boxes, making it difficult to trust their decisions in high-stakes scenarios. Traditional saliency maps help visualize model focus, but they frequently suffer from vanishing gradients and saturation, leading to misleading explanations. This text-only course guides you from the foundations of model interpretability to implementing robust, mathematically sound pixel attribution using Integrated Gradients. You will learn how to diagnose why a model made a specific prediction, ensuring your deep learning models are transparent, reliable, and explainable. What you'll learn: - Understand the core principles of Explainable AI and the limitations of traditional gradient-based saliency maps. - Explore the mathematics of Integrated Gradients, including key axioms like Completeness and Implementation Invariance. - Implement attribution algorithms from scratch using step-by-step written code walkthroughs in PyTorch. - Select and configure appropriate baselines for various image and text classification tasks. - Analyze model behavior to detect bias, debug unexpected predictions, and improve model trust. - Compare Integrated Gradients with other modern interpretability techniques to choose the right tool for your workflow. The course begins with foundational definitions of model interpretability and the mechanics of neural network attribution. You will then progress to the theory of path integrals, step-by-step implementation logic, and advanced validation strategies for deep learning models. Designed for data scientists, machine learning engineers, and developers, this course is accessible to beginners in explainability, requiring only a basic familiarity with neural networks and Python. Start reading today to unlock the black box of deep learning and make your model predictions explainable.