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.

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

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

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

Interpretability and explainability of data and machine learning models

moDel Agnostic Language for Exploration and eXplanation

🐢 The testing framework for ML models, from tabular to LLMs

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

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.

A collection of research materials on explainable AI/ML

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

Leave One Feature Out Importance

Code, exercises and tutorials of my personal blog ! 📝

OmniXAI: A Library for eXplainable AI

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.