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

High-Performance Symbolic Regression in Python and Julia

The Self-Coding System for Your App — Alan AI SDK for Web

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

Interpretability and explainability of data and machine learning models

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.

A collection of research materials on explainable AI/ML

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

Generate Diverse Counterfactual Explanations for any machine learning model.

moDel Agnostic Language for Exploration and eXplanation

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

XAI - An eXplainability toolbox for machine learning

A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.

Explainability for Vision Transformers

[CONTRIBUTORS WELCOME] Generalized Additive Models in Python

ICCV 2023-2025 Papers: Discover cutting-edge research from ICCV 2023-25, 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

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[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.

Leave One Feature Out Importance

Papers about explainability of GNNs

Code, exercises and tutorials of my personal blog ! 📝

Diffusion attentive attribution maps for interpreting Stable Diffusion.

Distributed High-Performance Symbolic Regression in Julia

Curated list of open source tooling for data-centric AI on unstructured data.

👋 Xplique is a Neural Networks Explainability Toolbox