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, Ph.D. from IIT Madras, is a Professor of Computer Science at B.M.S. 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 Ph.D. in from central University of Hyderabad in 2010 and working as Professor at 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.

Texte du rabat

The book discusses various partitioning strategies tailored for traditional machine learning algorithms. This book is a vital resource, illuminating the path towards unlocking the full potential of partitioning in shaping the future of AI technologies.

Contenu

Introduction to Partitioning Techniques 2. Partitioning Techniques for Deep Learning techniques 3. Graph based partitioning techniques 4. Partitioning techniques for Bigdata 5. Partitioning techniques for edge Computing