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Master Explainable AI: Interpreting Image Classifier Decisions

Name: Master Explainable AI: Interpreting Image Classifier Decisions
Price: 59 USD
Author: Puneet Mangla

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

Created by Puneet Mangla

Master Explainable AI: Interpreting Image Classifier Decisions

What's included

34 lessons across 5 modules
5 quizzes with instant feedback
2 projects in preconfigured environments
AI Tutor on every lesson

Join 3.1 million developers at

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

Learning Roadmap

34 Lessons1 Project5 Quizzes1 Assessment2 Challenges

Introduction to Explainable AI

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

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.

Miscellaneous Methods

6 Lessons

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

Metrics of Interpretability

7 Lessons

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

Final Assessment

Assessment

Weakly Supervised Object Localization

Project

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE 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.

Learn more about Puneet

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