Learning with AI : Intelligent Optimisation
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- FormatePub
- ISBN8230274933
- EAN9798230274933
- Date de parution29/12/2024
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurIndependently Published
Résumé
"Learning with AI: Intelligent Optimization" is a comprehensive guide that delves into the intersection of artificial intelligence and optimization techniques. The book is structured to cater to both newcomers and experienced practitioners in the field, offering a mix of theoretical foundations and practical applications. Overview:The first section introduces the fundamental concepts of intelligent optimization, explaining the key principles and methodologies underpinning the field.
Readers will gain insights into classic optimization techniques, such as linear and nonlinear programming, and be introduced to more advanced methods including heuristic and metaheuristic algorithms. Core Themes: Theoretical Foundations: The book covers the mathematical underpinnings of various optimization techniques and illustrates their frameworks, helping readers to understand when and how to apply them.
Machine Learning Integration: A significant portion focuses on integrating optimization within machine learning processes. It discusses how intelligent optimization can enhance model training, feature selection, and hyperparameter tuning in various machine learning algorithms. Practical Applications: Real-world case studies across different industries-such as supply chain management, finance, healthcare, and energy-illustrate the practical utility of intelligent optimization.
These examples showcase how organizations have successfully implemented optimization strategies to solve complex problems and improve efficiency. Tools and Technologies: The book introduces various software tools and programming libraries that facilitate intelligent optimization, providing readers with practical instructions to implement these techniques in their own projects. Future Directions: Lastly, the book explores emerging trends in artificial intelligence and optimization, including perspectives on quantum computing and their potential to revolutionize optimization problems.
Conclusion:"Learning with AI: Intelligent Optimization" stands out as an essential resource for anyone interested in harnessing the power of AI to tackle optimization challenges. The combination of in-depth theoretical knowledge and actionable insights equips readers to effectively apply intelligent optimization techniques in their fields, ultimately driving innovation and operational efficiency.
Readers will gain insights into classic optimization techniques, such as linear and nonlinear programming, and be introduced to more advanced methods including heuristic and metaheuristic algorithms. Core Themes: Theoretical Foundations: The book covers the mathematical underpinnings of various optimization techniques and illustrates their frameworks, helping readers to understand when and how to apply them.
Machine Learning Integration: A significant portion focuses on integrating optimization within machine learning processes. It discusses how intelligent optimization can enhance model training, feature selection, and hyperparameter tuning in various machine learning algorithms. Practical Applications: Real-world case studies across different industries-such as supply chain management, finance, healthcare, and energy-illustrate the practical utility of intelligent optimization.
These examples showcase how organizations have successfully implemented optimization strategies to solve complex problems and improve efficiency. Tools and Technologies: The book introduces various software tools and programming libraries that facilitate intelligent optimization, providing readers with practical instructions to implement these techniques in their own projects. Future Directions: Lastly, the book explores emerging trends in artificial intelligence and optimization, including perspectives on quantum computing and their potential to revolutionize optimization problems.
Conclusion:"Learning with AI: Intelligent Optimization" stands out as an essential resource for anyone interested in harnessing the power of AI to tackle optimization challenges. The combination of in-depth theoretical knowledge and actionable insights equips readers to effectively apply intelligent optimization techniques in their fields, ultimately driving innovation and operational efficiency.
"Learning with AI: Intelligent Optimization" is a comprehensive guide that delves into the intersection of artificial intelligence and optimization techniques. The book is structured to cater to both newcomers and experienced practitioners in the field, offering a mix of theoretical foundations and practical applications. Overview:The first section introduces the fundamental concepts of intelligent optimization, explaining the key principles and methodologies underpinning the field.
Readers will gain insights into classic optimization techniques, such as linear and nonlinear programming, and be introduced to more advanced methods including heuristic and metaheuristic algorithms. Core Themes: Theoretical Foundations: The book covers the mathematical underpinnings of various optimization techniques and illustrates their frameworks, helping readers to understand when and how to apply them.
Machine Learning Integration: A significant portion focuses on integrating optimization within machine learning processes. It discusses how intelligent optimization can enhance model training, feature selection, and hyperparameter tuning in various machine learning algorithms. Practical Applications: Real-world case studies across different industries-such as supply chain management, finance, healthcare, and energy-illustrate the practical utility of intelligent optimization.
These examples showcase how organizations have successfully implemented optimization strategies to solve complex problems and improve efficiency. Tools and Technologies: The book introduces various software tools and programming libraries that facilitate intelligent optimization, providing readers with practical instructions to implement these techniques in their own projects. Future Directions: Lastly, the book explores emerging trends in artificial intelligence and optimization, including perspectives on quantum computing and their potential to revolutionize optimization problems.
Conclusion:"Learning with AI: Intelligent Optimization" stands out as an essential resource for anyone interested in harnessing the power of AI to tackle optimization challenges. The combination of in-depth theoretical knowledge and actionable insights equips readers to effectively apply intelligent optimization techniques in their fields, ultimately driving innovation and operational efficiency.
Readers will gain insights into classic optimization techniques, such as linear and nonlinear programming, and be introduced to more advanced methods including heuristic and metaheuristic algorithms. Core Themes: Theoretical Foundations: The book covers the mathematical underpinnings of various optimization techniques and illustrates their frameworks, helping readers to understand when and how to apply them.
Machine Learning Integration: A significant portion focuses on integrating optimization within machine learning processes. It discusses how intelligent optimization can enhance model training, feature selection, and hyperparameter tuning in various machine learning algorithms. Practical Applications: Real-world case studies across different industries-such as supply chain management, finance, healthcare, and energy-illustrate the practical utility of intelligent optimization.
These examples showcase how organizations have successfully implemented optimization strategies to solve complex problems and improve efficiency. Tools and Technologies: The book introduces various software tools and programming libraries that facilitate intelligent optimization, providing readers with practical instructions to implement these techniques in their own projects. Future Directions: Lastly, the book explores emerging trends in artificial intelligence and optimization, including perspectives on quantum computing and their potential to revolutionize optimization problems.
Conclusion:"Learning with AI: Intelligent Optimization" stands out as an essential resource for anyone interested in harnessing the power of AI to tackle optimization challenges. The combination of in-depth theoretical knowledge and actionable insights equips readers to effectively apply intelligent optimization techniques in their fields, ultimately driving innovation and operational efficiency.