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A comprehensive collection of deep learning, reinforcement learning, and machine learning resources for vibe coders.
Advanced AI Explainability library for computer vision models built with PyTorch.
Interpret is a framework for fitting interpretable machine learning models and explaining black-box models.
A powerful debugging, monitoring and visualization tool for Python Machine Learning and Data Science workflows.
High-performance symbolic regression library for Python and Julia, with support for explainable AI.
The Alan AI SDK for Web provides a self-coding system for building AI-powered voice and conversational experiences in web apps.
A collection of research papers and software related to explainability in graph machine learning.
A Python library for interpretability and explainability of data and machine learning models.
A suite of tools that enable developers to build and monitor AI systems more responsibly.
Interpretable ML package for concise, transparent, and accurate predictive modeling.
Generate diverse counterfactual explanations for machine learning models.
A Python library for interpretable and explainable machine learning model analysis and visualization.
A library that provides seamless model explainability for transformers models in just 2 lines of code.
An explainability toolbox for developers building machine learning models with interpretability and fairness in mind.
A curated list of resources for the Conformal Prediction machine learning technique.
A library for explaining the decisions made by Vision Transformers, a type of AI model used for computer vision tasks.
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