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Master Explainable AI: Interpreting Image Classifier Decisions

Discover Explainable AI tools to interpret deep learning image classifiers. Lead the GenAI revolution by implementing and evaluating saliency maps, class activation maps, and interpretability metrics. Future-proof your skills.

Advanced

34 Lessons

Certificate of Completion

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

1 Project

1 Assessment

30 Playgrounds

5 Quizzes

Course Overview

What You'll Learn

Course Content

Apply Your Skills

Recommendations

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

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

Course Content

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

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

Course Author

Puneet Mangla

Project

Explainable AI: The Power of Interpreting ML Models

intermediate

40 min

Project

Explainable AI: The Power of Interpreting ML Models

Use explainable AI to interpret Logistic Regression, Random Forest, and Neural Networks, comparing global and local explanations using the UCI Census dataset.

intermediate

40 mins

Project

Implementation of U-Net for Image Segmentation

intermediate

2 hour

Project

Implementation of U-Net for Image Segmentation

Learn to implement U-Net for effective image segmentation using TensorFlow and Keras.

intermediate

2 hours

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