A game theoretic approach to explain the output of any machine learning model.
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Updated
Nov 10, 2026 - Jupyter Notebook
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A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
machine-learning data-mining awesome deep-learning awesome-list interpretability privacy-preserving production-machine-learning mlops privacy-preserving-machine-learning explainability responsible-ai machine-learning-operations ml-ops ml-operations privacy-preserving-ml large-scale-ml production-ml large-scale-machine-learning
Fit interpretable models. Explain blackbox machine learning.
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🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
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Interactive architecture diagrams for codebases
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[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.
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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.
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Make your agents talk human. ADHD-friendly output styles for Claude Code, Codex, and others. So you can pay attention, not tokens.
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蒸餾李宏毅老師的skill，結合Karpathy的LLM，Fable 5和GPT6 加持 以及 本人親自訪談
XAI - An eXplainability toolbox for machine learning
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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.
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Papers about explainability of GNNs
Power Tools for AI Engineers With Deadlines
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Shapley Values and Shapley Interactions for Machine Learning
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Visualization toolkit for neural networks in PyTorch! Demo -->
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Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet
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[Pattern Recognition 25] CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks
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Explain & debug any blackbox machine learning model with a single line of code.
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Official implementation of Score-CAM in PyTorch
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