Explaining dimensionality results using SHAP values
A curated list of awesome machine learning interpretability resources.
Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/
Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications
MindsDB is a Server for Artificial Intelligence Logic. Enabling developers to ship AI powered projects to production in a fast and scalable way.
moDel Agnostic Language for Exploration and eXplanation
Debugging, monitoring and visualization for Python Machine Learning and Data Science
💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)