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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.

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Overview

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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.

Explainable AI is a set of tools and frameworks that helps you understand and interpret the internal logic behind the prediction...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

A deep understanding of the need and benefits of Explainable AI

Content

34 Lessons1 Project5 Quizzes1 Assessment2 Challenges

Introduction to Explainable AI

5 Lessons

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

Course Overview

Explainable AI

Primer on Gradients

Coding Exercise on Derivatives

Introductory Quiz on Explainable AI

Explainable AI: The Power of Interpreting ML Models

Project

Saliency Maps

10 Lessons

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

Introduction to Saliency Maps—The Vanilla Gradient

Guided Backpropagation

Smooth Gradient Saliency

Integrated Gradients

LIME

Layer-wise Relevance Propagation

LRP-γ and LRP-Ɛ Rules

SHAP

Coding Exercise on Saliency Maps

Quiz on Saliency Maps

Class Activation Maps

6 Lessons

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

Introduction to Class Activation Maps

GradCAM

X-GradCAM

Eigen-CAM

Coding Exercise on Class Activation Maps

Quiz on Class Activation Maps

Miscellaneous Methods

6 Lessons

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

Counterfactual Explanations

Perturbation-Based Explanations

Concept Explanations

Prototypical Explanations

Coding Exercise on Miscellaneous Methods

Quiz on Miscellaneous Methods

Metrics of Interpretability

7 Lessons

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

Ground-Truth Faithfulness—Feature Agreement

Ground-Truth Faithfulness—Rank Correlation

Predictive Faithfulness

Stability and Fairness

Coding Exercise on Metrics of Interpretability

Quiz on Metrics of Interpretability

Course Conclusion

Final Assessment

Assessment

Weakly Supervised Object Localization

Project

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Puneet Mangla

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Select any text within any Educative course, and get an instant explanation — without ever leaving your browser.

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