├── .gitignore ├── README.md ├── award.png ├── code ├── 06.非監督學習也能做到可解釋性？探索XAI在非監督學習中的應用.ipynb ├── 07.KNN與XAI：從鄰居中找出模型的決策邏輯.ipynb ├── 08.解釋線性模型：探索線性迴歸和邏輯迴歸的可解釋性.ipynb ├── 09.基於樹狀結構的XAI方法：決策樹的可解釋性.ipynb ├── 10.Permutation_Importance：從特徵重要性角度解釋整個模型行為.ipynb ├── 11.Partial Dependence Plot：探索特徵對預測值的影響.ipynb ├── 13.LIME實作：實戰演練LIME解釋方法.ipynb ├── 15.SHAP實作：實戰演練SHAP解釋方法.ipynb ├── 17.解析深度神經網路：使用Deep SHAP進行模型解釋.ipynb ├── 19.Perturbation-Based：如何用擾動方法解釋神經網路.ipynb ├── 20.Gradient-Based：利用梯度訊息解釋神經網路.ipynb ├── 22.CAM-Based：如何解釋卷積神經網路.ipynb ├── 23.Attention-Based：使用注意力機制解釋CNN模型.ipynb ├── 24.LSTM的可解釋性：從時序資料解析人體姿態預測.ipynb ├── 26.XAI在表格型資料的應用：解析智慧工廠中的鋼材缺陷.ipynb ├── 27.XAI在NLP中的應用：以情感分析解釋語言模型.ipynb ├── dataset │ ├── banana.jpg │ ├── cat_dog.jpg │ ├── diabetes.csv │ ├── mHealth_subject1.csv │ ├── orange.jpg │ ├── pineapple.jpg │ └── stell-faults.csv ├── requirements.txt └── 使用SHAP解釋CNN模型.ipynb ├── design ├── FiraSans-Bold.otf ├── FiraSans-Italic.otf ├── FiraSans-Light.otf ├── FiraSans-LightItalic.otf ├── FiraSans-Regular.otf ├── cover.jpg ├── ebook-dark.png ├── pdf-dark.png ├── report.css ├── spotify-dark.svg └── youtube-dark.svg ├── docs ├── 1.揭開模型的神秘面紗:為何XAI對機器學習如此重要.md ├── 10.Permutation Importance:從特徵重要性角度解釋整個模型行為.md ├── 11.Partial Dependence Plot:探索特徵對預測值的影響.md ├── 12.LIME理論:如何用局部線性近似解釋黑箱模型.md ├── 13.LIME實作:實戰演練LIME解釋方法.md ├── 14.SHAP理論:解析SHAP解釋方法的核心.md ├── 15.SHAP實作:實戰演練SHAP解釋方法.md ├── 16.神經網路的可解釋性:如何理解深度學習中的黑箱模型.md ├── 17.解析深度神經網路:使用Deep SHAP進行模型解釋.md ├── 18.CNN卷積深度神經網路的解釋方法.md ├── 19.Perturbation Based如何用擾動方法解釋神經網路.md ├── 2.從黑盒到透明化:XAI技術的發展之路.md ├── 20.Gradient Based利用梯度訊息解釋神經網路.md ├── 21.Propagation Based探索反向傳播法的可解釋性.md ├── 22.CAM Based如何解釋卷積神經網路.md ├── 23.Attention Based使用注意力機制解釋CNN模型.md ├── 24.LSTM的可解釋性:解析步態分類中的時序資料.md ├── 25.XAI在影像處理中的瑕疵檢測:解釋卷積神經網路的運作.md ├── 26.智慧工廠製程中的鋼材缺陷檢測:運用XAI解析數值型感測器數據.md ├── 27.XAI在NLP中的應用:以情感分析解釋語言模型.md ├── 28.誤差分析和對抗樣本:如何利用XAI檢測模型的弱點.md ├── 29.XAI如何影響人類對技術的信任和接受程度.md ├── 3.機器學習中的可解釋性指標.md ├── 30.XAI未來發展方向:向更可靠的機器學習模型邁進.md ├── 4.LIME vs SHAP:哪種XAI解釋方法更適合你.md ├── 5.淺談XAI與傳統機器學習的區別.md ├── 6.非監督學習也能做到可解釋性-探索XAI在非監督學習中的應用.md ├── 7.KNN與XAI:從鄰居中找出模型的決策邏輯.md ├── 8.解釋線性模型:探索線性迴歸和邏輯迴歸的可解釋性.md ├── 9.基於樹狀結構的XAI方法:決策樹的可解釋性.md ├── google0a1389a852a5fccd.html ├── image │ ├── award.png │ ├── img1-1.png │ ├── img1-2.png │ ├── img1-3.png │ ├── img10-1.png │ ├── img10-2.gif │ ├── img10-3.png │ ├── img11-1.png │ ├── img11-2.png │ ├── img11-3.png │ ├── img11-4.png │ ├── img11-5.png │ ├── img11-6.png │ ├── img11-7.png │ ├── img11-8.png │ ├── img11-9.png │ ├── img12-1.png │ ├── img12-2.png │ ├── img12-3.png │ ├── img12-4.png │ ├── img12-5.png │ ├── img12-6.png │ ├── img12-7.png │ ├── img12-8.png │ ├── img13-1.png │ ├── img13-2.png │ ├── img13-3.png │ ├── img14-1.png │ ├── img14-10.png │ ├── img14-11.png │ ├── img14-2.png │ ├── img14-3.png │ ├── img14-4.png │ ├── img14-5.png │ ├── img14-6.png │ ├── img14-7.png │ ├── img14-8.png │ ├── img14-9.png │ ├── img15-1.png │ ├── img15-2.png │ ├── img15-3.png │ ├── img15-4.png │ ├── img15-5.png │ ├── img15-6.png │ ├── img15-7.png │ ├── img16-1.png │ ├── img16-2.png │ ├── img17-1.png │ ├── img17-2.png │ ├── img17-3.png │ ├── img17-4.png │ ├── img17-5.png │ ├── img17-6.png │ ├── img18-1.png │ ├── img18-2.png │ ├── img18-3.png │ ├── img19-1.gif │ ├── img19-10.png │ ├── img19-11.png │ ├── img19-12.png │ ├── img19-2.png │ ├── img19-3.png │ ├── img19-4.png │ ├── img19-5.png │ ├── img19-6.png │ ├── img19-7.png │ ├── img19-8.png │ ├── img19-9.png │ ├── img2-1.png │ ├── img2-2.png │ ├── img2-3.png │ ├── img2-4.png │ ├── img20-1.png │ ├── img20-2.png │ ├── img20-3.png │ ├── img20-4.png │ ├── img20-5.png │ ├── img21-1.png │ ├── img21-10.png │ ├── img21-2.png │ ├── img21-3.png │ ├── img21-4.png │ ├── img21-5.png │ ├── img21-6.png │ ├── img21-7.png │ ├── img21-8.png │ ├── img21-9.png │ ├── img22-1.png │ ├── img22-10.png │ ├── img22-11.png │ ├── img22-2.png │ ├── img22-3.png │ ├── img22-4.png │ ├── img22-5.png │ ├── img22-6.png │ ├── img22-7.png │ ├── img22-8.png │ ├── img22-9.png │ ├── img23-1.png │ ├── img23-2.png │ ├── img23-3.png │ ├── img23-4.png │ ├── img23-5.png │ ├── img23-6.png │ ├── img23-7.png │ ├── img23-8.png │ ├── img24-1.png │ ├── img24-2.png │ ├── img24-3.png │ ├── img24-4.png │ ├── img24-5.png │ ├── img24-6.png │ ├── img24-7.png │ ├── img24-8.png │ ├── img24-9.png │ ├── img25-1.png │ ├── img25-2.png │ ├── img25-3.png │ ├── img25-4.png │ ├── img25-5.png │ ├── img26-1.png │ ├── img26-2.png │ ├── img26-3.png │ ├── img26-4.png │ ├── img26-5.png │ ├── img26-6.png │ ├── img26-7.png │ ├── img26-8.png │ ├── img27-1.png │ ├── img27-2.png │ ├── img27-3.gif │ ├── img27-4.png │ ├── img28-1.png │ ├── img28-2.png │ ├── img28-3.png │ ├── img28-4.png │ ├── img29-1.png │ ├── img29-2.jpeg │ ├── img29-3.png │ ├── img3-1.png │ ├── img3-2.png │ ├── img3-3.png │ ├── img3-4.png │ ├── img30-1.png │ ├── img30-2.png │ ├── img30-3.png │ ├── img30-4.png │ ├── img4-1.png │ ├── img4-2.png │ ├── img4-3.png │ ├── img4-4.png │ ├── img4-5.png │ ├── img4-6.png │ ├── img5-1.png │ ├── 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Dependence Plot:探索特徵對預測值的影響.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/andy6804tw/crazyai-xai/HEAD/docs/11.Partial Dependence Plot:探索特徵對預測值的影響.md -------------------------------------------------------------------------------- /docs/12.LIME理論:如何用局部線性近似解釋黑箱模型.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/andy6804tw/crazyai-xai/HEAD/docs/12.LIME理論:如何用局部線性近似解釋黑箱模型.md -------------------------------------------------------------------------------- /docs/13.LIME實作:實戰演練LIME解釋方法.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/andy6804tw/crazyai-xai/HEAD/docs/13.LIME實作:實戰演練LIME解釋方法.md -------------------------------------------------------------------------------- /docs/14.SHAP理論:解析SHAP解釋方法的核心.md: -------------------------------------------------------------------------------- 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