explanations · GitHub Topics

#算法刷题#Popular algorithms explained in simple language with examples and links to their implementation in various programming languages and other required resources.

算法编程语言 Hacktoberfest 数据结构 explanations

3.09 k

10 个月前

SeldonIO / alibi

#计算机科学#Algorithms for explaining machine learning models

翻译 - 监视和解释机器学习模型的算法

机器学习 explanations interpretability counterfactual xai

Python 2.47 k

7 天前

ModelOriented / DALEX

#计算机科学#moDel Agnostic Language for Exploration and eXplanation

机器学习 interpretability 数据科学 xai model-visualization explanations explainable-ai predictive-modeling explainable-ml interpretable-machine-learning responsible-ai responsible-ml fairness

Python 1.41 k

1 个月前

PaddlePaddle / InterpretDL

InterpretDL: Interpretation of Deep Learning Models，基于『飞桨』的模型可解释性算法库。

paddlepaddle vision-transformer model-interpretation explanations grad-cam lime convolutional-neural-networks visualizations

Python 246

6 个月前

praveenpuglia / shadow-dom-in-depth

Everything you need to know about Shadow DOM

shadow-dom Web Components guide questions explanations Example custom-elements slot CSS

234

3 年前

liznerski / fcdd

#计算机科学#Repository for the Explainable Deep One-Class Classification paper

anomaly-detection 深度学习 one-class-learning xai explanations 机器学习 PyTorch Python

Python 227

2 年前

mwtarnowski / colmap-parameters

#博客#Some information about parameters and options available in COLMAP - SfM & MVS software. https://colmap.github.io

explanations cheatsheet notes

206

2 年前

altamiracorp / awesome-xai

#Awesome#Awesome Explainable AI (XAI) and Interpretable ML Papers and Resources

Awesome Lists xai explanations

156

4 年前

nishantc1527 / Algorithms-Java

#算法刷题#A collection of common algorithms and data structures implemented in Java.

explanations Java Python C++数据结构算法 trees trie bit-manipulation dynamic-programming graph-algorithms 人工智能神经网络 neural-networks sorting-algorithms interview-preparation 计算机科学

Java 138

3 年前

fhvilshoj / TorchLRP

A PyTorch 1.6 implementation of Layer-Wise Relevance Propagation (LRP).

PyTorch xai explanations

Python 133

4 年前

agitrubard / java-spring-best-practices

A repository dedicated to showcasing best practices in Java and Spring through concise code snippets.

best-practices clean-code code-snippets explanations Java Maven Open Source Spring Spring Boot 依赖注入

Java 105

3 个月前

dylan-slack / Fooling-LIME-SHAP

#计算机科学#Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)

adversarial-attacks lime shap interpretability 机器学习 explanations Bukkit

Jupyter Notebook 82

2 年前

mertyg / debug-mistakes-cce

Meaningfully debugging model mistakes with conceptual counterfactual explanations. ICML 2022

concepts explanations interpretability

Python 75

3 年前

wilsonjr / ClusterShapley

#计算机科学#Explaining dimensionality results using SHAP values

机器学习 explainable-ai explainable-ml dimensionality-reduction explanations visualizations

C++ 53

2 个月前

akarasman / yolo-heatmaps

A utility for generating heatmaps of YOLOv8 using Layerwise Relevance Propagation (LRP/CRP).

yolo xai 机器视觉 object-detection heatmap explainable-ai explainable-ml explanations Python

Jupyter Notebook 49

1 年前

Jawz84 / explainpowershell

PowerShell version of explainshell.com

PowerShell explanations language 工具 Azure

C# 36

15 天前

BirkhoffG / Explainable-ML-Papers

#计算机科学#A list of research papers of explainable machine learning.

机器学习 xai Bukkit academic research interpretability explainability explainable-ml interpretable-ml explanations survey Awesome Lists human-in-the-loop

4 年前

dmitrykazhdan / MARLeME

General-purpose library for extracting interpretable models from Multi-Agent Reinforcement Learning systems

reinforcement-learning marl multi-agent-reinforcement-learning interpretability explainability open-ai knowledge-extraction plug-and-play Library lime explanations rl xai

C++ 20

5 年前

cmu-transparency / aaai-2021-tutorial

Anupam Datta, Matt Fredrikson, Klas Leino, Kaiji Lu, Shayak Sen, Zifan Wang

fairness explanations robustness 隐私

4 年前

jpmorganchase / cf-shap

#计算机科学#Counterfactual SHAP: a framework for counterfactual feature importance

explainability explainable-ai explainable-ml explanations feature-attribution feature-importance 机器学习 shap shapley xgboost

HTML 17

2 年前