Distributed Machine Learning And Computing

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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.

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.

Distributed Machine Learning On Edge Computing Systems

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Author : Di Wu
language : en
Publisher:
Release Date : 2024

Distributed Machine Learning On Edge Computing Systems written by Di Wu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.

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.

Scaling Up Machine Learning

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Author : Ron Bekkerman
language : en
Publisher: Cambridge University Press
Release Date : 2011-12-30

Scaling Up Machine Learning written by Ron Bekkerman and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-12-30 with Computers categories.

This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algorithms, and deep dives into several applications, make the book equally useful for researchers, students and practitioners.

Distributed Computing And Artificial Intelligence 15th International Conference

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Author : Fernando De La Prieta
language : en
Publisher: Springer
Release Date : 2018-07-04

Distributed Computing And Artificial Intelligence 15th International Conference written by Fernando De La Prieta and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-04 with Technology & Engineering categories.

The 15th International Symposium on Distributed Computing and Artificial Intelligence 2018 (DCAI 2018) is a forum to present applications of innovative techniques for studying and solving complex problems. The exchange of ideas between scientists and technicians from both the academic and industrial sector is essential to facilitate the development of systems that can meet the ever-increasing demands of today’s society. The present edition brings together past experience, current work and promising future trends associated with distributed computing, artificial intelligence and their application in order to provide efficient solutions to real problems. This symposium is organized by the University of Castilla-La Mancha, the Osaka Institute of Technology and the University of Salamanca. The present edition was held in Toledo, Spain, from 20th – 22nd June, 2018.

Edge Learning For Distributed Big Data Analytics

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Author : Song Guo
language : en
Publisher: Cambridge University Press
Release Date : 2022-02-10

Edge Learning For Distributed Big Data Analytics written by Song Guo and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-10 with Computers categories.

Introduces fundamental theory, basic and advanced algorithms, and system design issues. Essential reading for experienced researchers and developers, or for those who are just entering the field.

Advances In Distributed Computing And Machine Learning

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Author : Binayak Kar
language : en
Publisher: Springer Nature
Release Date : 2025-10-02

Advances In Distributed Computing And Machine Learning written by Binayak Kar 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 a collection of peer-reviewed best selected research papers presented at the Sixth International Conference on Advances in Distributed Computing and Machine Learning (ICADCML 2025), organized by Dept. of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan during January 9–10, 2025. This book presents recent innovations in the field of scalable distributed systems in addition to cutting-edge research in the field of Internet of Things (IoT) and blockchain in distributed environments. The work is presented in two volumes.

Advances In Distributed Computing And Machine Learning

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Author : Umakanta Nanda
language : en
Publisher: Springer Nature
Release Date : 2024-08-02

Advances In Distributed Computing And Machine Learning written by Umakanta Nanda and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-02 with Computers categories.

This book is a collection of peer-reviewed best selected research papers presented at the Fifth International Conference on Advances in Distributed Computing and Machine Learning (ICADCML 2024), organized by School of Electronics and Engineering, VIT - AP University, Amaravati, Andhra Pradesh, India, during 5–6 January 2024. This book presents recent innovations in the field of scalable distributed systems in addition to cutting edge research in the field of Internet of Things (IoT) and blockchain in distributed environments.

Distributed Computing And Artificial Intelligence Special Sessions Ii 15th International Conference

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Author : Sigeru Omatu
language : en
Publisher: Springer
Release Date : 2019-06-20

Distributed Computing And Artificial Intelligence Special Sessions Ii 15th International Conference written by Sigeru Omatu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-20 with Computers categories.

This book addresses a broad range of topics, from newly proposed techniques in Artificial Intelligence (AI) and Machine Learning to various applications such as decision-making, pattern classification for data, image and signals, robotics, and control systems. Big data applications are discussed, while improved methods and wholly new methods for using deep learning technologies are also presented. The topics covered are comprehensive and reflect a wide range of technologies in the area. In particular, the latest methods in deep learning approaches and applications are discussed in many parts of the book, providing a better understanding of these new technologies. The book’s general scope includes the latest methods in the areas of Artificial Intelligence and Machine Learning for use in distributed computing as well as decision support systems. As the book covers a rather wide area, its intended readership ranges from those working in AI and machine learning technologies to those working on applications utilizing these technologies, researchers new to these areas who need background information on the technologies and applications, and more experienced researchers looking for new methods and applications.