machine learning data processing de wang xiang sheng (10 résultats)

Hardcover. Etat : Brand New. In Stock.

Hardcover. Etat : new. Hardcover. Machine learning has become a cornerstone of modern data-driven science and technology. For mathematics students and researchers, understanding the mathematical foundations behind machine learning is essential, even if they never work directly with real-world datasets.This book provides a rigorous yet accessible introduction to the core mathematical ideas that underpin machine learning. Topics such as linear and nonlinear regression, regularization techniques, and the fundamentals of neural networks are explained in detail from a clear mathematical perspective.Unlike many existing texts that emphasize coding and practical implementation, this book focuses on theoretical results and conceptual understanding. It is designed for readers who want to grasp the mathematics behind machine learning without writing code.Who should read this book?Mathematics students and researchers interested in machine learning but with little programming experience.Scientists and engineers who have applied machine learning in practice and now seek a deeper understanding of its mathematical foundations. tab-stops: list .5in;">Scientists and engineers who have applied machine learning in practice and now seek a deeper understanding of its mathematical foundations. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Etat : NEW.

Etat : New.

Hardcover. Etat : new. Hardcover. Machine learning has become a cornerstone of modern data-driven science and technology. For mathematics students and researchers, understanding the mathematical foundations behind machine learning is essential, even if they never work directly with real-world datasets.This book provides a rigorous yet accessible introduction to the core mathematical ideas that underpin machine learning. Topics such as linear and nonlinear regression, regularization techniques, and the fundamentals of neural networks are explained in detail from a clear mathematical perspective.Unlike many existing texts that emphasize coding and practical implementation, this book focuses on theoretical results and conceptual understanding. It is designed for readers who want to grasp the mathematics behind machine learning without writing code.Who should read this book?Mathematics students and researchers interested in machine learning but with little programming experience.Scientists and engineers who have applied machine learning in practice and now seek a deeper understanding of its mathematical foundations. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Gebunden. Etat : New.

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning has become a cornerstone of modern data-driven science and technology. For mathematics students and researchers, understanding the mathematical foundations behind machine learning is essential, even if they never work directly with real-world datasets.This book provides a rigorous yet accessible introduction to the core mathematical ideas that underpin machine learning. Topics such as linear and nonlinear regression, regularization techniques, and the fundamentals of neural networks are explained in detail from a clear mathematical perspective.Unlike many existing texts that emphasize coding and practical implementation, this book focuses on theoretical results and conceptual understanding. It is designed for readers who want to grasp the mathematics behind machine learning without writing code.Who should read this book Mathematics students and researchers interested in machine learning but with little programming experience.Scientists and engineers who have applied machine learning in practice and now seek a deeper understanding of its mathematical foundations.

Hardcover. Etat : new. Hardcover. Machine learning has become a cornerstone of modern data-driven science and technology. For mathematics students and researchers, understanding the mathematical foundations behind machine learning is essential, even if they never work directly with real-world datasets.This book provides a rigorous yet accessible introduction to the core mathematical ideas that underpin machine learning. Topics such as linear and nonlinear regression, regularization techniques, and the fundamentals of neural networks are explained in detail from a clear mathematical perspective.Unlike many existing texts that emphasize coding and practical implementation, this book focuses on theoretical results and conceptual understanding. It is designed for readers who want to grasp the mathematics behind machine learning without writing code.Who should read this book?Mathematics students and researchers interested in machine learning but with little programming experience.Scientists and engineers who have applied machine learning in practice and now seek a deeper understanding of its mathematical foundations. tab-stops: list .5in;">Scientists and engineers who have applied machine learning in practice and now seek a deeper understanding of its mathematical foundations. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Machine learning has become a cornerstone of modern data-driven science and technology. For mathematics students and researchers, understanding the mathematical foundations behind machine learning is essential, even if they never work directly with real-world datasets.This book provides a rigorous yet accessible introduction to the core mathematical ideas that underpin machine learning. Topics such as linear and nonlinear regression, regularization techniques, and the fundamentals of neural networks are explained in detail from a clear mathematical perspective.Unlike many existing texts that emphasize coding and practical implementation, this book focuses on theoretical results and conceptual understanding. It is designed for readers who want to grasp the mathematics behind machine learning without writing code.Who should read this book Mathematics students and researchers interested in machine learning but with little programming experience.Scientists and engineers who have applied machine learning in practice and now seek a deeper understanding of its mathematical foundations. 119 pp. Englisch.

Buch. Etat : Neu. Machine Learning in Data Processing | Xiang-Sheng Wang (u. a.) | Buch | Forum for Interdisciplinary Mathematics | xiii | Englisch | 2026 | Springer | EAN 9783032208545 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.