Linear and Nonlinear Regression in Artificial Intelligenc VOL-1: Mathematical Foundations, Regularization Techniques & Predictive Modeling
Linear and Nonlinear Regression in Artificial Intelligence VOL-1 explores the mathematical foundations, regularization techniques, and predictive modeling essential for AI development. This book bridges theory and application, providing a strong groundwork for understanding regression methods in intelligent systems. Ideal for learners and experts in AI and mathematics.
About This Book
This book provides a comprehensive introduction to linear and nonlinear regression within the context of artificial intelligence. It covers the essential mathematical foundations that underpin these regression methods, offering readers a solid theoretical base.
Regularization techniques are examined in detail, highlighting their role in preventing overfitting and improving model generalization in AI systems. The content is structured to build understanding progressively from basic concepts to advanced applications.
Predictive modeling is a key focus, demonstrating how regression approaches contribute to accurate forecasting and decision-making in artificial intelligence. This volume serves as a foundational resource for those studying AI and mathematics.
Designed for students, researchers, and professionals, the book emphasizes practical implications of theoretical knowledge in real-world AI scenarios. It encourages a deeper appreciation of how math drives innovation in intelligence technologies.
Reviews
No reviews yet. Be the first to review this book!