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Master Explainable AI: Interpreting Image Classifier Decisions
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.
5.0
34 Lessons
2 Projects
7h
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- A deep understanding of the need and benefits of Explainable AI
- The ability to design and implement popular explanation algorithms
- Hands-on experience combining existing explanation methods to generate more robust explanations
- An understanding of explainers used to interpret the decision of a neural network
- The ability to evaluate and quantify the quality of the neural network explanations
Learning Roadmap
1.
Introduction to Explainable AI
Introduction to Explainable AI
Get familiar with Explainable AI to understand and implement transparent, interpretable AI systems.
2.
Saliency Maps
Saliency Maps
Unpack the core of various saliency map techniques to interpret image classifier decisions.
3.
Class Activation Maps
Class Activation Maps
6 Lessons
6 Lessons
Work your way through Class Activation Maps, GradCAM, X-GradCAM, Eigen-CAM, and Ablation-CAM techniques.
4.
Miscellaneous Methods
Miscellaneous Methods
6 Lessons
6 Lessons
Apply your skills to various advanced methods for interpreting AI image classifiers.
5.
Metrics of Interpretability
Metrics of Interpretability
7 Lessons
7 Lessons
Dig into interpretability metrics for AI, feature agreement, rank correlation, predictive faithfulness, and fairness.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
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.
ABOUT THE AUTHOR
Puneet Mangla
Data and Applied Scientist at Microsoft Advertising working on Ad quality checks. As a part-time technical writer, I love teaching machine learning concepts through blogs and courses.
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