Discover Explainable AI tools to interpret deep learning classifiers. Use saliency maps, activation maps, and metrics to lead the GenAI revolution and future-proof your skills.

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Explainable AI is a set of tools and frameworks that helps you understand and interpret the internal logic behind the predictions made by a deep learning network. With this, you can generate insights into the behavior and working of the model to mitigate issues around it in the development phase.

In this course, you will be introduced to popular Explainable AI algorithms such as smooth gradient, integrated gradient, LIME, class activation maps, counterfactual explanations, feature attributions, etc., for image classification networks such as MobileNet-V2 trained on large-scale datasets like ImageNet-1K.

By the end of this course, you will understand the need for Explainable AI and be able to design and implement popular explanation algorithms like saliency maps, class activation maps, counterfactual explanations, etc. You will be able to evaluate and quantify the quality of the neural network explanations via several interpretability metrics.

Master Explainable AI: Interpreting Image Classifier Decisions

 X_n(i,j)  = X(i,j) + \alpha \cdot M_k(i, j) \cdot \mu_n(i,j) \ \text{ where } \ \mu_n(i,j) \sim \N(0,1)

Learn to evaluate the stability of an explanation using the relative input stability (RIS) metric.


Introduction to Explainable AI

Explainable AI: The Power of Interpreting ML Models

Saliency Maps

Class Activation Maps

Miscellaneous Methods

Metrics of Interpretability

Final Assessment

Weakly Supervised Object Localization

Stability and Fairness

Relative input stability (RIS)