Difference Equations and Machine Learning - Rilegato

Stipanovic, Duan

Rilegato

ISBN 10: 303200909X ISBN 13: 9783032009098

Casa editrice: Springer Nature, 2025

Riassunto

This book presents in-depth explanations of well-known and recognized behaviors of neural networks in machine learning.  In addition, the author provides novel technical analyses of behaviors of discrete-time dynamical systems modeled as difference equations.  These analyses and their outcomes are closely related to models of very well-known neural networks such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks, which are widely used in machine learning and artificial intelligence (AI) applications.  The author also discusses difference equations and their relevance to neural networks, machine learning, and AI.

Su questo libro

Informazioni sull'autore

Dušan Stipanovic, Ph.D., is a Professor in the Coordinated Science Laboratory and Department of Industrial and Enterprise Systems Engineering at University of Illinois Urbana-Champaign.  He received his B.S. in Electrical Engineering from the University of Belgrade, Serbia and his M.S.E.E. and Ph.D. in Electrical Engineering from Santa Clara University, California.  Dr. Stipanovic’s research interests include differential and difference equations, control and stability theory, neural networks, and differential games with applications in control of autonomous vehicles, machine learning and AI, precision agriculture, circuits, and medical robotics.

Dalla quarta di copertina

This book presents in-depth explanations of well-known and recognized behaviors of neural networks in machine learning.  In addition, the author provides novel technical analyses of behaviors of discrete-time dynamical systems modeled as difference equations.  These analyses and their outcomes are closely related to models of very well-known neural networks such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks, which are widely used in machine learning and artificial intelligence (AI) applications.  The author also discusses difference equations and their relevance to neural networks, machine learning, and AI.

 In addition, this book:

• Includes characterizations of difference equations and technical prospectives of discrete-time systems
• Provides new insights into the dynamical behaviors of some of the most popular neural networks used in machine learning
• Discusses novel technical analyses of discrete-time dynamical systems modeled as difference equations

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 303200909X
• ISBN 13: 9783032009098
• Rilegatura: Copertina rigida
• Numero di pagine: 150
• Contatto del produttore: GPSR Kontakt; ProductSafety@springernature.com; ; Tiergartenstr. 17; Heidelberg; 69121; Germania