thampiman / interpretable-ai-bookLinks
Code associated with my Interpretable AI Book (https://www.manning.com/books/interpretable-ai)
☆64Updated 4 years ago
Alternatives and similar repositories for interpretable-ai-book
Users that are interested in interpretable-ai-book are comparing it to the libraries listed below
Sorting:
- Explainable AI with Python, published by Packt☆165Updated last week
- ☆91Updated 2 years ago
- Overview of different model interpretability libraries.☆51Updated 3 years ago
- Code and documentation for experiments in the TreeExplainer paper☆190Updated 6 years ago
- Code and Content for Manning Publication on Graph Neural Networks☆118Updated 11 months ago
- ☆214Updated 4 years ago
- Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)☆154Updated 3 years ago
- Rule Extraction Methods for Interactive eXplainability☆48Updated 3 years ago
- Interpretable Machine Learning with Python, published by Packt☆475Updated last week
- Code used to obtain results for my medium articles☆76Updated 2 years ago
- ☆33Updated last year
- 🌀 Stanford CS 228 - Probabilistic Graphical Models☆92Updated 7 years ago
- Course material for 1RT700 Statistical Machine Learning☆63Updated last month
- The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).☆223Updated last month
- Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)☆84Updated 3 years ago
- Applied Machine Learning Explainability Techniques, published by Packt☆248Updated last week
- A collection of resources for learning and research.☆97Updated 9 months ago
- Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification☆98Updated 2 years ago
- Code and notebook repository for the book Evolutionary Deep Learning by Micheal Lanham☆65Updated 3 years ago
- ☆94Updated 3 years ago
- CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms☆298Updated 2 years ago
- ☆30Updated last year
- TimeSHAP explains Recurrent Neural Network predictions.☆196Updated 2 years ago
- This course is an overview of applied causal inference.☆56Updated 8 months ago
- Notes for Judea Pearl et al., *Causal Inference in Statistics, a Primer*☆68Updated 6 years ago
- Graph Machine Learning, published by Packt☆339Updated last week
- All about explainable AI, algorithmic fairness and more☆110Updated 2 years ago
- Explaining Anomalies Detected by Autoencoders Using SHAP☆45Updated 4 years ago
- Notes, exercises and other materials related to causal inference, causal discovery and causal ML.☆162Updated last month
- Repository for the results of my master thesis, about the generation and evaluation of synthetic data using GANs☆45Updated 2 years ago