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English | 2022 | ISBN: 9781617297649 | MP3@64 kbps | Duration: 8h 38m | 712 MB
Subject: Computers, Science & Technology, Engineering, Technology, Artificial Intelligence (AI), Robotics & Artificial Intelligence, Artificial Intelligence - General
Catergory: Computers, Science & Technology, Engineering, Technology, Artificial Intelligence (AI), Robotics & Artificial Intelligence, Artificial Intelligence - General
Publisher: eBookIt.com
Description:
Quote
AI doesn't have to be a black box. These practical techniques help shine a light on your model's mysterious inner workings. Make your AI more transparent, and you'll improve trust in your results, combat data leakage and bias, and ensure compliance with legal requirements.
In Interpretable AI, you will learn
Why AI models are hard to interpret
Interpreting white box models such as linear regression, decision trees, and generalized additive models
Partial dependence plots, LIME, SHAP and Anchors, and other techniques such as saliency mapping, network dissection, and representational learning
What fairness is and how to mitigate bias in AI systems
Implement robust AI systems that are GDPR-compliant
Interpretable AI opens up the black box of your AI models. It teaches cutting-edge techniques and best practices that can make even complex AI systems interpretable. Each method is easy to implement with just Python and open source libraries. You'll learn to identify when you can utilize models that are inherently transparent, and how to mitigate opacity when your problem demands the power of a hard-to-interpret deep learning model.
In Interpretable AI, you will learn
Why AI models are hard to interpret
Interpreting white box models such as linear regression, decision trees, and generalized additive models
Partial dependence plots, LIME, SHAP and Anchors, and other techniques such as saliency mapping, network dissection, and representational learning
What fairness is and how to mitigate bias in AI systems
Implement robust AI systems that are GDPR-compliant
Interpretable AI opens up the black box of your AI models. It teaches cutting-edge techniques and best practices that can make even complex AI systems interpretable. Each method is easy to implement with just Python and open source libraries. You'll learn to identify when you can utilize models that are inherently transparent, and how to mitigate opacity when your problem demands the power of a hard-to-interpret deep learning model.
�� Contents of Download:
�� Appendix A.Getting Set Up.mp3 (04:55) (2023) (9.03 MB)
�� Appendix B.PyTorch.mp3 (23:05) (2023) (34 MB)
�� Chapter 1.Introduction.mp3 (38:08) (2023) (54.67 MB)
�� Chapter 2.White Box Models.mp3 (59:02) (2023) (83.35 MB)
�� Chapter 3.Model Agnostic Methods Global Interpretability.mp3 (48:02) (2023) (68.25 MB)
�� Chapter 4.Model Agnostic Methods Local Interpretability.mp3 (01:12:17) (2023) (101.55 MB)
�� Chapter 5.Saliency Mapping.mp3 (01:05:20) (2023) (92.01 MB)
�� Chapter 6.Understanding Layers And Units.mp3 (01:00:18) (2023) (85.09 MB)
�� Chapter 7.Understanding Semantic Similarity.mp3 (01:02:30) (2023) (88.11 MB)
�� Chapter 8.Fairness And Mitigating Bias.mp3 (01:02:22) (2023) (87.94 MB)
�� Chapter 9.Path To Explainable AI.mp3 (18:17) (2023) (27.4 MB)
�� Part 1.Interpretability Basics.mp3 (00:56) (2023) (3.57 MB)
�� Part 2.Interpreting Model Processing.mp3 (01:31) (2023) (4.37 MB)
�� Part 3.Interpreting Model Representations.mp3 (00:49) (2023) (3.4 MB)
�� Part 4.Fairness And Bias.mp3 (00:51) (2023) (3.47 MB)
�� Duration: 08:38:23
�� BitRate: 192 Kbps
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⭐️ Interpretable AI Audiobook ✅ (746.22 MB)
RapidGator Link(s)
https://rapidgator.net/file/77c0e67782fb7ae921c02f8e0c2e13ea/Interpretable.AI.Audiobook.rar
