Debugging Machine Learning Models with Python
Develop high-performance, low-bias, and explainable machine learning and deep learning models
1st Edition
Published on 21. May 2024
344 pages
978-1-80020-113-2 (ISBN)
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Ali Madani
Debugging Machine Learning Models with Python
Develop high-performance, low-bias, and explainable machine learning and deep learning models
Book
09/2023
Packt Publishing
€84.60
Shipment within 3-4 weeks
Persons
Madani Ali :
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.
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
Table of Contents - Beyond Code Debugging
- 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
- 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
