xplique
by deel-ai
👋 Xplique is a Neural Networks Explainability Toolbox
AI summary
Neural network interpreter
An Explainable AI toolbox that provides various methods and tools to understand and interpret the behavior of neural networks
- stars
- 654
- forks
- 53
- watching
- 12
Similar projects
Found by comparing what the projects do, not just their names.
ethicalml/xai1.1K
Bias analysis tool
An eXplainability toolbox for machine learning that enables data analysis and model evaluation to mitigate biases and improve performance
Interpreter
Provides tools for interpreting deep neural networks
XAI library
A collection of resources and papers related to Explainable Artificial Intelligence (XAI) for machine learning model interpretability.
Explainability toolkit
An eXplainable AI toolkit for evaluating and interpreting neural network explanations in various deep learning frameworks.
AI Explainability Tool
A toolkit for explaining complex AI models and data-driven insights
XAI tool
A Python package providing an explainable AI interface to research projects
Explainer
An R package and workshop materials for explaining machine learning models using explainable AI techniques
AI resources
An online repository providing resources and information on explainable AI, algorithmic fairness, ML security, and related topics
AI library
A collection of algorithms and data structures for artificial intelligence and machine learning in Swift
csinva/imodels1.4K
Interpretable model library
An open-source package that provides interpretable machine learning models compatible with scikit-learn.
Neural Network Library
A Python library for implementing and training various neural network architectures
Model interpreter
Provides tools and techniques for interpreting machine learning models
Neural Explanation Tools
Provides an implementation of Hierarchical explanations for Neural Network predictions
AI toolkit
A toolbox of AI modules written in Swift for various machine learning tasks and algorithms
interpretml/dice1.4K
Model explanations
Provides counterfactual explanations for machine learning models to facilitate interpretability and understanding.
