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Interpretable Machine Learning with Python - Second Edition

You're reading from  Interpretable Machine Learning with Python - Second Edition

Product type Book
Published in Oct 2023
Publisher Packt
ISBN-13 9781803235424
Pages 606 pages
Edition 2nd Edition
Languages
Concepts
Author (1):
Serg Masís Serg Masís
Profile icon Serg Masís
LinkedIn
Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's a climate and agronomic data scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a start-up, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making—and machine learning interpretation helps bridge this gap robustly.
See other products by Serg Masís

Table of Contents (17) Chapters

Preface Interpretation, Interpretability, and Explainability; and Why Does It All Matter? Key Concepts of Interpretability Interpretation Challenges Global Model-Agnostic Interpretation Methods Local Model-Agnostic Interpretation Methods Anchors and Counterfactual Explanations Visualizing Convolutional Neural Networks Interpreting NLP Transformers Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis Feature Selection and Engineering for Interpretability Bias Mitigation and Causal Inference Methods Monotonic Constraints and Model Tuning for Interpretability Adversarial Robustness What’s Next for Machine Learning Interpretability? Other Books You May Enjoy
Index

Summary

After reading this chapter, you should understand how attacks can be perpetrated on machine learning models and evasion attacks in particular. You should know how to perform FSGM, BIM, PGD, C&W, and AP attacks, as well as how to defend against them with spatial smoothing and adversarial training. Last but not least, you know how to evaluate adversarial robustness.

The next chapter is the last one, and it outlines some ideas on what’s next for machine learning interpretation.

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Authors (1)

Profile icon Serg Masís
LinkedIn
Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's a climate and agronomic data scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a start-up, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making—and machine learning interpretation helps bridge this gap robustly.
Read more
See other products by Serg Masís

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