| 001 Case Study-1 Demo of What-if Tool (WIT).mp4 | 103.7 MB | ||
| 001 Case Study-1 Demo of What-if Tool (WIT)_en.srt | 19 KB | ||
| 001 Demonstration of Glass Box Models Part-1.mp4 | 51.8 MB | ||
| 001 Demonstration of Glass Box Models Part-1_en.srt | 10.9 KB | ||
| 001 Gratitude.mp4 | 26.7 MB | ||
| 001 Gratitude_en.srt | 2.6 KB | ||
| 001 Interaction Demos of LRP.mp4 | 93.1 MB | ||
| 001 Interaction Demos of LRP_en.srt | 10.5 KB | ||
| 001 LIME Working Principle.mp4 | 65.8 MB | ||
| 001 LIME Working Principle_en.srt | 13.1 KB | ||
| 001 Open your gift.html | 204.8 B | ||
| 001 Other online courses from the Instructor.html | 3.7 KB | ||
| 001 SHAP Working Principle.mp4 | 48.9 MB | ||
| 001 SHAP Working Principle_en.srt | 12.2 KB | ||
| 001 Useful Resources for XAI.html | 5.9 KB | ||
| 001 Working Principle and Applications of Contrastive Explanations Method (CEM).mp4 | 66.4 MB | ||
| 001 Working Principle and Applications of Contrastive Explanations Method (CEM)_en.srt | 12.2 KB | ||
| 001 Working Principle of Counterfactual Explanations-1.mp4 | 44.8 MB | ||
| 001 Working Principle of Counterfactual Explanations-1_en.srt | 9.1 KB | ||
| 001 XAI in Action.mp4 | 103.6 MB | ||
| 001 XAI in Action_en.srt | 13 KB | ||
| 002 Case Study-2 Demo of What-if Tool (WIT).mp4 | 96 MB | ||
| 002 Case Study-2 Demo of What-if Tool (WIT)_en.srt | 16.2 KB | ||
| 002 Demonstration of Glass Box Models Part-2.mp4 | 35.3 MB | ||
| 002 Demonstration of Glass Box Models Part-2_en.srt | 5.3 KB | ||
| 002 Mathematical Modelling of LIME Part-1.mp4 | 50.2 MB | ||
| 002 Mathematical Modelling of LIME Part-1_en.srt | 11.6 KB | ||
| 002 Mathematical Modelling of SHAP Part-1.mp4 | 37.3 MB | ||
| 002 Mathematical Modelling of SHAP Part-1_en.srt | 8.1 KB | ||
| 002 Need and Importance of XAI.mp4 | 79.5 MB | ||
| 002 Need and Importance of XAI_en.srt | 11.8 KB | ||
| 002 Text Books from the Instructor.html | 1.3 KB | ||
| 002 Working Principle of Counterfactual Explanations.mp4 | 46.4 MB | ||
| 002 Working Principle of Counterfactual Explanations_en.srt | 9.8 KB | ||
| 002 Working Principle of LRP.mp4 | 36.1 MB | ||
| 002 Working Principle of LRP_en.srt | 6.7 KB | ||
| 003 By Design Interpretable Models Decision Tree Glass Box Models.mp4 | 54.8 MB | ||
| 003 By Design Interpretable Models Decision Tree Glass Box Models_en.srt | 11.9 KB | ||
| 003 Case Study-3 Demo of What-if Tool (WIT).mp4 | 84 MB | ||
| 003 Case Study-3 Demo of What-if Tool (WIT)_en.srt | 11.1 KB | ||
| 003 Mathematical Modelling of Counterfactual Explanations.mp4 | 71.6 MB | ||
| 003 Mathematical Modelling of Counterfactual Explanations_en.srt | 14.9 KB | ||
| 003 Mathematical Modelling of LIME Part-2.mp4 | 55.8 MB | ||
| 003 Mathematical Modelling of LIME Part-2_en.srt | 13 KB | ||
| 003 Mathematical Modelling of LRP.mp4 | 84.1 MB | ||
| 003 Mathematical Modelling of LRP_en.srt | 17.7 KB | ||
| 003 Mathematical Modelling of SHAP Part-2.mp4 | 53.7 MB | ||
| 003 Mathematical Modelling of SHAP Part-2_en.srt | 10.9 KB | ||
| 003 Need for Train-Test Split.mp4 | 118.3 MB | ||
| 003 Need for Train-Test Split_en.srt | 18 KB | ||
| 004 By Design Interpretable Models Logistic Regression Glass Box Models.mp4 | 50.6 MB | ||
| 004 By Design Interpretable Models Logistic Regression Glass Box Models_en.srt | 11.5 KB | ||
| 004 Case Study-4 Demo of What-if Tool (WIT).mp4 | 74 MB | ||
| 004 Case Study-4 Demo of What-if Tool (WIT)_en.srt | 10.4 KB | ||
| 004 Demo of LIME for tabular Stroke Dataset.mp4 | 93.3 MB | ||
| 004 Demo of LIME for tabular Stroke Dataset_en.srt | 14 KB | ||
| 004 Demo of LRP on MRI dataset Part-1.mp4 | 70.6 MB | ||
| 004 Demo of LRP on MRI dataset Part-1_en.srt | 10.4 KB | ||
| 004 Global Counterfactuals.mp4 | 26.9 MB | ||
| 004 Global Counterfactuals_en.srt | 4.4 KB | ||
| 004 Mathematical Modelling of SHAP Part-3.mp4 | 76.4 MB | ||
| 004 Mathematical Modelling of SHAP Part-3_en.srt | 16.5 KB | ||
| 004 Techniques for Balancing the Dataset.mp4 | 37 MB | ||
| 004 Techniques for Balancing the Dataset_en.srt | 8.7 KB | ||
| 005 Black Box Models Part-1.mp4 | 30.5 MB | ||
| 005 Black Box Models Part-1_en.srt | 6.8 KB | ||
| 005 Case Study-5 Demo of What-if Tool (WIT).mp4 | 106.9 MB | ||
| 005 Case Study-5 Demo of What-if Tool (WIT)_en.srt | 12.5 KB | ||
| 005 Code for Balancing the Dataset.mp4 | 33.2 MB | ||
| 005 Code for Balancing the Dataset_en.srt | 4.8 KB | ||
| 005 Demo of Counterfactual Explanations on Stroke Dataset.mp4 | 172.8 MB | ||
| 005 Demo of Counterfactual Explanations on Stroke Dataset_en.srt | 26.8 KB | ||
| 005 Demo of LRP on MRI dataset Part-2.mp4 | 75.9 MB | ||
| 005 Demo of LRP on MRI dataset Part-2_en.srt | 12.8 KB | ||
| 005 LIME Demonstration for textual dataset Part-1.mp4 | 64.2 MB | ||
| 005 LIME Demonstration for textual dataset Part-1_en.srt | 11.7 KB | ||
| 005 SHAP Demonstration.mp4 | 190.4 MB | ||
| 005 SHAP Demonstration_en.srt | 25.1 KB | ||
| 006 Black Box Models Part-2.mp4 | 45.1 MB | ||
| 006 Black Box Models Part-2_en.srt | 7.8 KB | ||
| 006 LIME Demonstration for textual dataset Part-2.mp4 | 71.3 MB | ||
| 006 LIME Demonstration for textual dataset Part-2_en.srt | 11.5 KB | ||
| 006 Quality Metrics for Classification Confusion Matrix, Precision, Recall, F1Score.mp4 | 57.5 MB | ||
| 006 Quality Metrics for Classification Confusion Matrix, Precision, Recall, F1Score_en.srt | 13.5 KB | ||
| 006 Recommended Practice Tasks.html | 102.4 B | ||
| 007 Demo of Data Exploration for Stroke Dataset.mp4 | 65.5 MB | ||
| 007 Demo of Data Exploration for Stroke Dataset_en.srt | 11.1 KB | ||
| 007 LIME Demonstration for textual dataset Part-3.mp4 | 134.7 MB | ||
| 007 LIME Demonstration for textual dataset Part-3_en.srt | 20.3 KB | ||
| 007 XAI Categorization.mp4 | 37.1 MB | ||
| 007 XAI Categorization_en.srt | 7.8 KB | ||
| 008 InterpretML Package.mp4 | 56.9 MB | ||
| 008 InterpretML Package_en.srt | 8.1 KB | ||
| 008 Recommended Practice Tasks.html | 921.6 B | ||
| 009 Demo for Logistic Regression Model Explanation.mp4 | 76.2 MB | ||
| 009 Demo for Logistic Regression Model Explanation_en.srt | 10.7 KB | ||
| 010 Demo for Decision Tree Classifier Explanation.mp4 | 130.5 MB | ||
| 010 Demo for Decision Tree Classifier Explanation_en.srt | 22.5 KB | ||
| 011 Explainable Boosting Classifier Working Principle.mp4 | 40.3 MB | ||
| 011 Explainable Boosting Classifier Working Principle_en.srt | 9.5 KB | ||
| 012 Demo for Explainable Boosting Classifier Explanaation.mp4 | 77.8 MB | ||
| 012 Demo for Explainable Boosting Classifier Explanaation_en.srt | 12.2 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| external-assets-links.txt | 409.6 B | ||
| ▲ 112 total files | |||
Explainable Artificial Intelligence (XAI) with Python
https://DevCourseWeb.com
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 56 lectures (7h 56m) | Size: 3.07 GB
Simplified Way to Learn XAI
What you'll learn
Importance of XAI in modern world
Differentiation of glass box, white box and black box ML models
Categorization of XAI on the basis of their scope, agnosticity, data types and explanation techniques
Trade-off between accuracy and interpretability
Application of InterpretML package from Microsoft to generate explanations of ML models
Need of counterfactual and contrastive explanations
Working principles and mathematical modeling of XAI techniques like LIME, SHAP, DiCE, LRP, counterfactual and contrastive explanationss
Application of XAI techniques like LIME, SHAP, DiCE, LRP to generate explanations for black-box models for tabular, textual, and image datasets.
What-if tool from Google to analyze data points and to generate counterfactuals
Requirements
No programming experience needed. You will learn everything you need to know to apply XAI for generating explanations for ML models.
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 1.4 GB | freecoursewb | 1 year | 3 | 0 |
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