Deep Learning Model Explainability

Deep learning models are powerful but often opaque, making decisions hard to interpret. This course teaches you how to explain neural networks using methods like saliency maps, Grad-CAM, SHAP, and more to make models transparent and trustworthy.

What you'll learn

Deep learning models have revolutionized AI but remain notoriously opaque, making it difficult to understand how decisions are made. In this course, Deep Learning Model Explainability, you’ll learn to interpret and explain complex neural networks to build trust and improve transparency.

First, you’ll explore the unique challenges of explainability in deep architectures like CNNs, RNNs, and transformers. Next, you’ll learn how to apply powerful explainability techniques such as saliency maps, Grad-CAM, integrated gradients, SHAP, and LIME to visualize and analyze model decisions. Finally, you’ll discover how to evaluate the practical limitations and assumptions behind these methods and choose the right explanation strategy for your use case.

When you’re finished with this course, you’ll have the skills and knowledge needed to confidently explain and interpret deep learning models in real-world applications.