A Data Odyssey
Data exploration, interpretable machine learning, explainable AI and algorithm fairness
Taxonomy of Explainable AI Methods in Computer Vision | Free XAI Course
Build Class Activation Maps (CAMs) from Scratch with Python & PyTorch Hooks | Free XAI Course
Understanding Class Activation Maps (CAMs) for Deep Learning Interpretability | Free XAI Course
Implementing Guided Backpropagation from Scratch | PyTorch Hooks & Deep Learning Interpretability
Guided Backpropagation theory | FREE Explainable AI (XAI) Course with Python
Grad-CAM with Python | FREE XAI Course | L7 - Gradient-weighted Class Activation Mapping
Grad-CAM Explained | FREE XAI Course | L7 - Gradient-weighted Class Activation Mapping
Debugging a Pot Plant Detector | FREE Python Course | L1 - The Importance of XAI in Computer Vision
Applying Permutation Channel Importance (PCI) to a Remote Sensing Model | Python Tutorial
Explaining Computer Vision Models with PCI
Explaining Anomalies with Isolation Forest and SHAP | Python Tutorial
SHAP with CatBoostClassifier for Categorical Features | Python Tutorial
Applying LIME with Python | Local & Global Interpretations
An introduction to LIME for local interpretations | Intuition and Algorithm |
Friedman's H-statistic Python Tutorial | Artemis Package
Friedman's H-statistic for Analysing Interactions | Maths and Intuition
Accumulated Local Effect Plots (ALEs) | Explanation & Python Code
PDPs and ICE Plots | Python Code | scikit-learn Package
Partial Dependence (PDPs) and Individual Conditional Expectation (ICE) Plots | Intuition and Math
Permutation Feature Importance from Scratch | Explanation & Python Code
Model Agnostic Methods for XAI | Global v.s. Local | Permutation v.s. Surrogate Models
8 Plots for Explaining Linear Regression | Residuals, Weight, Effect & SHAP
Feature Selection using Hierarchical Clustering | Python Tutorial
8 Characteristics of a Good Machine Learning Feature | Predictive, Variety, Interpretability, Ethics
Interpretable Feature Engineering | How to Build Intuitive Machine Learning Features
Modelling Non-linear Relationships with Regression
Explaining Machine Learning to a Non-technical Audience
Get more out of Explainable AI (XAI): 10 Tips
The 6 Benefits of Explainable AI (XAI) | Improve accuracy, decrease harm and tell better stories
Introduction to Explainable AI (XAI) | Interpretable models, agnostic methods, counterfactuals