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.

Advanced

34 Lessons

7h

Certificate of Completion

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.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

1 Project
1 Assessment
30 Playgrounds
5 Quizzes

This course includes

1 Project
1 Assessment
30 Playgrounds
5 Quizzes

Course Overview

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 i...Show More

What You'll Learn

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

What You'll Learn

A deep understanding of the need and benefits of Explainable AI

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Course Content

1.

Introduction to Explainable AI

Get familiar with Explainable AI to understand and implement transparent, interpretable AI systems.

Explainable AI: The Power of Interpreting ML Models

Project

2.

Saliency Maps

Unpack the core of various saliency map techniques to interpret image classifier decisions.
3.

Class Activation Maps

Work your way through Class Activation Maps, GradCAM, X-GradCAM, Eigen-CAM, and Ablation-CAM techniques.
4.

Miscellaneous Methods

Apply your skills to various advanced methods for interpreting AI image classifiers.
5.

Metrics of Interpretability

Dig into interpretability metrics for AI, feature agreement, rank correlation, predictive faithfulness, and fairness.

Final Assessment

Assessment

Weakly Supervised Object Localization

Project

Course Author

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Anthony Walker

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Emma Bostian šŸž

@EmmaBostian

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Anthony Walker

@_webarchitect_

Emma Bostian šŸž

@EmmaBostian

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