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Full Description
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 memory consumption, and enhance overall efficiency.
Graphs are prevalent in various AI domains. This book is specifically designed for graph data structures using partitioning techniques and also explores insights into optimizing graph algorithms and analytics. With the explosion of data, efficient partitioning becomes crucial for processing large datasets. This book discusses various partitioning techniques that enable effective management and analysis of big data, enhancing speed and resource utilization. Edge computing demands resource-efficient strategies. It examines partitioning methods tailored for edge devices, enabling AI capabilities at the edge while addressing resource. This book showcases how partitioning techniques have been successfully applied across various AI domains. It demonstrates real-world scenarios where partitioning optimizes AI algorithms and systems.
By bridging the gap between theory and practical applications, this book intends to equip researchers, practitioners, and students with invaluable insights into harnessing partitioning for optimizing AI-driven systems, data processing, and problem-solving strategies. It describes the various advantages and disadvantages of partitioning techniques. This book is a vital resource, illuminating the path towards unlocking the full potential of partitioning in shaping the future of AI technologies.
Contents
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
