Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Fit interpretable models. Explain blackbox machine learning.

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots

Debugging, monitoring and visualization for Python Machine Learning and Data Science

Generative AI SDK for Web to build AI Assistants for apps built with JavaScript, React, Angular, Vue, Ember, Electron

A collection of research papers and software related to explainability in graph machine learning.

High-Performance Symbolic Regression in Python and Julia

Interpretability and explainability of data and machine learning models

moDel Agnostic Language for Exploration and eXplanation

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

A collection of research materials on explainable AI/ML

Generate Diverse Counterfactual Explanations for any machine learning model.

Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

XAI - An eXplainability toolbox for machine learning

ICCV 2023 Papers: Discover cutting-edge research from ICCV 2023, the leading computer vision conference. Stay updated on the latest in computer vision and deep learning, with code included. ⭐ support visual intelligence development!

OmniXAI: A Library for eXplainable AI

Leave One Feature Out Importance