Search results for "Distributed Optimization and Learning"
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Innovations in Optimization and Machine Learning
In today's rapidly evolving world, businesses are confronted with the complex task of streamlining their operations, utilizing machine learning to their advantage, and maneuvering through the intricacies of artificial intelligence. It has become increasingly essential to allocate resources effectively, make informed decisions based on data, and capitalize on AI technologies. However, many organizations require assistance in understanding these disciplines' theoretical principles, practical implementations, and ethical implications. Innovations in Optimization and Machine Learning serve as a comprehensive solution, offering a deep dive into optimization, machine learning, and AI. By unraveling the complexities and providing practical insights, it empowers researchers, practitioners, students, and enthusiasts to understand and contribute to advancing these fields. The book covers many topics, from evolutionary algorithms to ethical AI development, ensuring a thorough understanding of key concepts and their real-world implications. By bridging the gap between theory and practice, this book equips readers with the knowledge and tools to address optimization, machine learning, and AI challenges. Whether you're looking to enhance operational efficiency, develop innovative solutions, or drive meaningful change, this book is your guide to unlocking the transformative potential of optimization, machine learning, and AI in today's dynamic landscape.
R 14,092.77
Innovations in Optimization and Machine Learning
In today's rapidly evolving world, businesses are confronted with the complex task of streamlining their operations, utilizing machine learning to their advantage, and maneuvering through the intricacies of artificial intelligence. It has become increasingly essential to allocate resources effectively, make informed decisions based on data, and capitalize on AI technologies. However, many organizations require assistance in understanding these disciplines' theoretical principles, practical implementations, and ethical implications. Innovations in Optimization and Machine Learning serve as a comprehensive solution, offering a deep dive into optimization, machine learning, and AI. By unraveling the complexities and providing practical insights, it empowers researchers, practitioners, students, and enthusiasts to understand and contribute to advancing these fields. The book covers many topics, from evolutionary algorithms to ethical AI development, ensuring a thorough understanding of key concepts and their real-world implications. By bridging the gap between theory and practice, this book equips readers with the knowledge and tools to address optimization, machine learning, and AI challenges. Whether you're looking to enhance operational efficiency, develop innovative solutions, or drive meaningful change, this book is your guide to unlocking the transformative potential of optimization, machine learning, and AI in today's dynamic landscape.
R 10,645.92
Machine Learning Hybridization and Optimization for Intelligent Applications
This book discusses state-of-the-art reviews of the existing machine-learning techniques and algorithms including hybridizations and optimizations. It covers applications of machine learning via artificial intelligence (AI) prediction tools, the discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, pattern recognition approaches to functional magnetic resonance imaging, image and speech recognition, automatic language translation, medical diagnostic, stock market prediction, traffic prediction, and product automation.Features:• Focuses on hybridization and optimization of machine learning techniques.• Reviews supervised, unsupervised and reinforcement learning using case study based applications.• Covers latest machine learning applications in diverse domains as IoT, data science, cloud computing, distributed and parallel computing.• Explains computing models using real world examples and dataset based experiments.• Includes case study based explanations and usage for machine learning technologies and applicationsThis book is aimed at graduate students and researchers in machine learning, artificial intelligence, and electrical engineering.
R 9,330.54
Derivative-Free Optimization
This book offers a pioneering exploration of classification-based derivative-free optimization (DFO), providing researchers and professionals in artificial intelligence, machine learning, AutoML, and optimization with a robust framework for addressing complex, large-scale problems where gradients are unavailable. By bridging theoretical foundations with practical implementations, it fills critical gaps in the field, making it an indispensable resource for both academic and industrial audiences. The book introduces innovative frameworks such as sampling-and-classification (SAC) and sampling-and-learning (SAL), which underpin cutting-edge algorithms like Racos and SRacos. These methods are designed to excel in challenging optimization scenarios, including high-dimensional search spaces, noisy environments, and parallel computing. A dedicated section on the ZOOpt toolbox provides practical tools for implementing these algorithms effectively. The book's structure moves from foundational principles and algorithmic development to advanced topics and real-world applications, such as hyperparameter tuning, neural architecture search, and algorithm selection in AutoML. Readers will benefit from a comprehensive yet concise presentation of modern DFO methods, gaining theoretical insights and practical tools to enhance their research and problem-solving capabilities. A foundational understanding of machine learning, probability theory, and algorithms is recommended for readers to fully engage with the material.
R 6,996.69
