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Interpretable Machine Learning with Python

You're reading from   Interpretable Machine Learning with Python Learn to build interpretable high-performance models with hands-on real-world examples

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Product type Paperback
Published in Mar 2021
Publisher Packt
ISBN-13 9781800203907
Length 736 pages
Edition 1st Edition
Languages
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Author (1):
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Serg Masís Serg Masís
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Serg Masís
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Introduction to Machine Learning Interpretation
2. Chapter 1: Interpretation, Interpretability, and Explainability; and Why Does It All Matter? FREE CHAPTER 3. Chapter 2: Key Concepts of Interpretability 4. Chapter 3: Interpretation Challenges 5. Section 2: Mastering Interpretation Methods
6. Chapter 4: Fundamentals of Feature Importance and Impact 7. Chapter 5: Global Model-Agnostic Interpretation Methods 8. Chapter 6: Local Model-Agnostic Interpretation Methods 9. Chapter 7: Anchor and Counterfactual Explanations 10. Chapter 8: Visualizing Convolutional Neural Networks 11. Chapter 9: Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis 12. Section 3:Tuning for Interpretability
13. Chapter 10: Feature Selection and Engineering for Interpretability 14. Chapter 11: Bias Mitigation and Causal Inference Methods 15. Chapter 12: Monotonic Constraints and Model Tuning for Interpretability 16. Chapter 13: Adversarial Robustness 17. Chapter 14: What's Next for Machine Learning Interpretability? 18. Other Books You May Enjoy

Dataset sources

  • Cabani, A., Hammoudi, K., Benhabiles, H. and Melkemi, M. "MaskedFace-Net - A dataset of correctly/incorrectly masked face images in the context of COVID-19", Smart Health, ISSN 2352-6483, Elsevier, 2020. https://doi.org/10.1016/j.smhl.2020.100144 (Creative Commons BY-NC-SA 4.0 license by NVIDIA Corporation)
  • Karras, T., Laine, S. and Aila, T. (2019). A Style-Based Generator Architecture for Generative Adversarial Networks. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 4396-4405. https://arxiv.org/abs/1812.04948 (Creative Commons BY-NC-SA 4.0 license by NVIDIA Corporation)
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