Guided Backpropagation and Saliency Maps for Deep Learning Interpretability

Deep learning models are often criticized as black boxes, making it hard to trust their decisions. Saliency maps solve this by highlighting exactly which pixels an image classifier focuses on to make a prediction. By learning how to visualize these decisions, you can debug your models, build trust with stakeholders, and ensure your neural networks are learning the right features. In this course, you will learn how to demystify neural network decisions. You will start with the foundational concepts of model interpretability, then proceed to write clean Python code to implement guided backpropagation, handle ReLU activations, and utilize modern interpretability frameworks to explain your models. What you'll learn: - Understand the core principles of neural network interpretability and saliency mapping. - Implement backpropagation from scratch to calculate gradients with respect to input images. - Master the mechanics of guided backpropagation by suppressing negative gradients in ReLU activations. - Compare guided backpropagation with modern attribution methods like Grad-CAM. - Use popular interpretability libraries like Captum in PyTorch to evaluate model decisions. - Debug and analyze image classifier predictions through clear, text-guided code examples. The course begins with essential terminology and the mathematical intuition behind gradients, before guiding you step-by-step through custom gradient modification and practical interpretability workflows. This text-only course is designed for beginners in machine learning and computer vision who have a basic grasp of Python; no advanced deep learning background is required. Start reading today to make your deep learning models transparent and trustworthy.