فهرست مطالب

• Brief content
• Contents
• Preface
• Acknowledgments
• About this book
• About the author
• About the cover illustration
• Part 1: Interpretability basics
• Chapter 1: Introduction
• 1.1 Diagnostics+ AI—an example AI system
• 1.2 Types of machine learning systems
• 1.3 Building Diagnostics+ AI
• 1.4 Gaps in Diagnostics+ AI
• 1.5 Building a robust Diagnostics+ AI system
• 1.6 Interpretability vs. explainability
• 1.7 What will I learn in this book?
• Chapter 2: White-box models
• 2.1 White-box models
• 2.2 Diagnostics+—diabetes progression
• 2.3 Linear regression
• 2.4 Decision trees
• 2.5 Generalized additive models (GAMs)
• 2.6 Looking ahead to black-box models
• Part 2: Interpreting model processing
• Chapter 3: Model-agnostic methods: Global interpretability
• 3.1 High school student performance predictor
• 3.2 Tree ensembles
• 3.3 Interpreting a random forest
• 3.4 Model-agnostic methods: Global interpretability
• Chapter 4: Model-agnostic methods: Local interpretability
• 4.1 Diagnostics+ AI: Breast cancer diagnosis
• 4.2 Exploratory data analysis
• 4.3 Deep neural networks
• 4.4 Interpreting DNNs
• 4.5 LIME
• 4.6 SHAP
• 4.7 Anchors
• Chapter 5: Saliency mapping
• 5.1 Diagnostics+ AI: Invasive ductal carcinoma detection
• 5.2 Exploratory data analysis
• 5.3 Convolutional neural networks
• 5.4 Interpreting CNNs
• 5.5 Vanilla backpropagation
• 5.6 Guided backpropagation
• 5.7 Other gradient-based methods
• 5.8 Grad-CAM and guided Grad-CAM
• 5.9 Which attribution method should I use?
• Part 3: Interpreting model representations
• Chapter 6: Understanding layers and units
• 6.1 Visual understanding
• 6.2 Convolutional neural networks: A recap
• 6.3 Network dissection framework
• 6.4 Interpreting layers and units
• Chapter 7: Understanding semantic similarity
• 7.1 Sentiment analysis
• 7.2 Exploratory data analysis
• 7.3 Neural word embeddings
• 7.4 Interpreting semantic similarity
• Part 4: Fairness and bias
• Chapter 8: Fairness and mitigating bias
• 8.1 Adult income prediction
• 8.2 Fairness notions
• 8.3 Interpretability and fairness
• 8.4 Mitigating bias
• 8.5 Datasheets for datasets
• Chapter 9: Path to explainable AI
• 9.1 Explainable AI
• 9.2 Counterfactual explanations
• Appendix A: Getting set up
• Appendix B: PyTorch
• Index