1st Edition

Advances in Partitioning Techniques A Prospective towards Artificial Intelligence

134 Pages 6 B/W Illustrations
by Chapman & Hall

134 Pages 6 B/W Illustrations
by Chapman & Hall

This book discusses various partitioning strategies tailored for traditional machine learning algorithms. It examines how data can be divided efficiently to enhance the performance and scalability of classic machine learning models. It explores how partitioning methods can be applied to neural networks and other deep learning architectures and describes various ways to accelerate training, reduce... Read more

1. Introduction to partitioning techniques 2. Partitioning techniques for deep learning techniques 3. Graph-based partitioning techniques 4. Partitioning techniques for Big Data 5. Partitioning techniques for edge computing

Biography

Shankru Guggari is a machine learning specialist who primarily focuses on enhancing the performance of machine learning techniques. His research interests include pattern recognition, explainable AI, and machine learning. He has published his work in various international conferences and journals and has over four years of academic experience.

Umadevi V, PhD from IIT Madras, is a Professor of Computer Science at BMS College of Engineering, Bangalore and a Senior IEEE member. She has published extensively in reputed journals and conferences and received grants for research in medical thermography.

Vijaya Kumar Kadappa obtained his PhD in from the Central University of Hyderabad in 2010 and working as Professor at the Department of Computer Applications, BMS College of Engineering, Bangalore. He has 30+ research publications. Kadappa is a life member of IUPR-AI, ISTE, and CSI.