
Debugging Machine Learning Models with Python
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Persons
Ali Madani worked as the director of machine learning at Cyclica Inc leading AI technology development front of Cyclica for drug discovery before acquisition of Cyclica by Recursion Pharmaceuticals. Ali completed his PhD at University of Toronto focusing on machine learning modeling in cancer setting and attained a Master of Mathematics from the University of Waterloo. As a believer in industry-oriented education and pro-democratization of knowledge, Ali has actively educated students and professionals through international workshops and courses on basic and advanced high-quality machine learning modeling. When not immersed in machine learning modeling and teaching, Ali enjoys exercising, cooking and traveling with his partner.
Content
- Machine Learning Life Cycle
- Debugging toward Responsible AI
- Detecting Performance and Efficiency Issues in Machine Learning Models
- Improving the Performance of Machine Learning Models
- Interpretability and Explainability in Machine Learning Modeling
- Decreasing Bias and Achieving Fairness
- Controlling Risks Using Test-Driven Development
- Testing and Debugging for Production
- Versioning and Reproducible Machine Learning Modeling
- Avoiding and Detecting Data and Concept Drifts
- Going Beyond ML Debugging with Deep Learning
- Advanced Deep Learning Techniques
- Introduction to Recent Advancements in Machine Learning
- Correlation versus Causality
- Security and Privacy in Machine Learning
- Human-in-the-Loop Machine Learning
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- E-reader: Bookeen, Kobo, Pocketbook, Sony, Tolino and many more (not Kindle).
The file format ePUB works well for novels and non-fiction books – i.e., 'flowing' text without complex layout. On an e-reader or smartphone, line and page breaks automatically adjust to fit the small displays.
This eBook does not use copy protection or Digital Rights Management
For more information, see our eBook Help page.