Distributed Machine Learning And Gradient Optimization
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Distributed Machine Learning And Gradient Optimization
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Author : Jiawei Jiang
language : en
Publisher: Springer Nature
Release Date : 2022-02-23
Distributed Machine Learning And Gradient Optimization written by Jiawei Jiang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-23 with Computers categories.
This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol. Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appeal to a broad audience in the field of machine learning, artificial intelligence, big data and database management.
Optimization Algorithms For Distributed Machine Learning
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Author : Gauri Joshi
language : en
Publisher: Springer Nature
Release Date : 2022-11-25
Optimization Algorithms For Distributed Machine Learning written by Gauri Joshi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-25 with Computers categories.
This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime.
Scalable And Distributed Machine Learning And Deep Learning Patterns
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Author : Thomas, J. Joshua
language : en
Publisher: IGI Global
Release Date : 2023-08-25
Scalable And Distributed Machine Learning And Deep Learning Patterns written by Thomas, J. Joshua and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-25 with Computers categories.
Scalable and Distributed Machine Learning and Deep Learning Patterns is a practical guide that provides insights into how distributed machine learning can speed up the training and serving of machine learning models, reduce time and costs, and address bottlenecks in the system during concurrent model training and inference. The book covers various topics related to distributed machine learning such as data parallelism, model parallelism, and hybrid parallelism. Readers will learn about cutting-edge parallel techniques for serving and training models such as parameter server and all-reduce, pipeline input, intra-layer model parallelism, and a hybrid of data and model parallelism. The book is suitable for machine learning professionals, researchers, and students who want to learn about distributed machine learning techniques and apply them to their work. This book is an essential resource for advancing knowledge and skills in artificial intelligence, deep learning, and high-performance computing. The book is suitable for computer, electronics, and electrical engineering courses focusing on artificial intelligence, parallel computing, high-performance computing, machine learning, and its applications. Whether you're a professional, researcher, or student working on machine and deep learning applications, this book provides a comprehensive guide for creating distributed machine learning, including multi-node machine learning systems, using Python development experience. By the end of the book, readers will have the knowledge and abilities necessary to construct and implement a distributed data processing pipeline for machine learning model inference and training, all while saving time and costs.
Proceedings Of The 2025 3rd International Conference On Image Algorithms And Artificial Intelligence Iciaai 2025
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Author : Yanan Sun
language : en
Publisher: Springer Nature
Release Date : 2025-10-02
Proceedings Of The 2025 3rd International Conference On Image Algorithms And Artificial Intelligence Iciaai 2025 written by Yanan Sun and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-02 with Computers categories.
This book is an open access. The 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025) will be held in Singapore (Online Participation is acceptable) during May 23-25 2025, bringing together researchers, scientists, and industry experts to discuss groundbreaking advancements in image processing, algorithmic development, and artificial intelligence. This conference offers a dynamic platform to exchange ideas, form partnerships, and explore emerging research in AI.As AI technology becomes an integral part of industries worldwide, its transformative potential is shaping modern society and redefining fields like healthcare, finance, manufacturing, and education. The integration of deep learning, neural networks, and computer vision is driving AI to new heights, enabling machines to perform tasks that once required human intelligence. From autonomous systems to predictive analytics, the impact of AI continues to grow, bringing both unprecedented opportunities and unique challenges.ICIAAI was established to address these developments, providing a platform where experts and innovators can present solutions, explore ethical considerations, and discuss AI’s role in the future. The first two editions of ICIAAI were highly successful, attracting a global audience and showcasing pioneering work in machine learning, computer vision, data-driven algorithms, and more. The second edition saw a significant expansion in topics and participation, reflecting the surging interest in AI’s applications and societal impact.
Robust Machine Learning
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Author : Rachid Guerraoui
language : en
Publisher: Springer Nature
Release Date : 2024-04-04
Robust Machine Learning written by Rachid Guerraoui and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-04 with Mathematics categories.
Today, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may misbehave and jeopardize the learning procedure. This could, for example, result from hardware and software bugs, data poisoning or a malicious player controlling a subset of the machines. This book explains in simple terms what it means for a distributed machine learning scheme to be robust to these threats, and how to build provably robust machine learning algorithms. Studying the robustness of machine learning algorithms is a necessity given the ubiquity of these algorithms in both the private and public sectors. Accordingly, over the past few years, we have witnessed a rapid growth in the number of articles published on the robustness of distributed machine learning algorithms. We believe it is time to provide a clear foundation to this emerging and dynamic field. By gathering the existing knowledge and democratizing the concept of robustness, the book provides the basis for a new generation of reliable and safe machine learning schemes. In addition to introducing the problem of robustness in modern machine learning algorithms, the book will equip readers with essential skills for designing distributed learning algorithms with enhanced robustness. Moreover, the book provides a foundation for future research in this area.
Ecai 2020
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Author : Giuseppe De Giacomo
language : en
Publisher: SAGE Publications Limited
Release Date : 2020-09-15
Ecai 2020 written by Giuseppe De Giacomo and has been published by SAGE Publications Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-15 with Computers categories.
This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of more than 1,700 submissions was received for ECAI 2020, of which 1,443 were reviewed. Of these, 361 full-papers and 36 highlight papers were accepted (an acceptance rate of 25% for full-papers and 45% for highlight papers). The book is divided into three sections: ECAI full papers; ECAI highlight papers; and PAIS papers. The topics of these papers cover all aspects of AI, including Agent-based and Multi-agent Systems; Computational Intelligence; Constraints and Satisfiability; Games and Virtual Environments; Heuristic Search; Human Aspects in AI; Information Retrieval and Filtering; Knowledge Representation and Reasoning; Machine Learning; Multidisciplinary Topics and Applications; Natural Language Processing; Planning and Scheduling; Robotics; Safe, Explainable, and Trustworthy AI; Semantic Technologies; Uncertainty in AI; and Vision. The book will be of interest to all those whose work involves the use of AI technology.
Siam Journal On Control And Optimization
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Author : Society for Industrial and Applied Mathematics
language : en
Publisher:
Release Date : 1998
Siam Journal On Control And Optimization written by Society for Industrial and Applied Mathematics and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Automatic control categories.
Distributed Optimization And Learning
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Author : Zhongguo Li
language : en
Publisher: Elsevier
Release Date : 2024-07-18
Distributed Optimization And Learning written by Zhongguo Li and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-18 with Technology & Engineering categories.
Distributed Optimization and Learning: A Control-Theoretic Perspective illustrates the underlying principles of distributed optimization and learning. The book presents a systematic and self-contained description of distributed optimization and learning algorithms from a control-theoretic perspective. It focuses on exploring control-theoretic approaches and how those approaches can be utilized to solve distributed optimization and learning problems over network-connected, multi-agent systems. As there are strong links between optimization and learning, this book provides a unified platform for understanding distributed optimization and learning algorithms for different purposes. - Provides a series of the latest results, including but not limited to, distributed cooperative and competitive optimization, machine learning, and optimal resource allocation - Presents the most recent advances in theory and applications of distributed optimization and machine learning, including insightful connections to traditional control techniques - Offers numerical and simulation results in each chapter in order to reflect engineering practice and demonstrate the main focus of developed analysis and synthesis approaches
Dissertation Abstracts International
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Author :
language : en
Publisher:
Release Date : 2008
Dissertation Abstracts International written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Dissertations, Academic categories.
Distributed Machine Learning And Computing
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Author : M. Hadi Amini
language : en
Publisher: Springer Nature
Release Date : 2024-05-28
Distributed Machine Learning And Computing written by M. Hadi Amini and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-28 with Technology & Engineering categories.
This book focuses on a wide range of distributed machine learning and computing algorithms and their applications in healthcare and engineering systems. The contributors explore how these techniques can be applied to different real-world problems. It is suitable for students and researchers interested in conducting research in multidisciplinary areas that rely on distributed machine learning and computing techniques.