Oneflow For Parallel And Distributed Deep Learning Systems
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Oneflow For Parallel And Distributed Deep Learning Systems
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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-07-12
Oneflow For Parallel And Distributed Deep Learning Systems written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-12 with Computers categories.
"OneFlow for Parallel and Distributed Deep Learning Systems" In a rapidly evolving landscape of machine learning infrastructure, "OneFlow for Parallel and Distributed Deep Learning Systems" provides a comprehensive and authoritative exploration of the OneFlow framework as a cornerstone for large-scale deep learning. Through an expert survey of distributed learning architectures, the book delves into OneFlow’s core system principles, innovative design philosophies, and its architectural evolution in comparison to platforms like TensorFlow, PyTorch, Horovod, and MXNet. It thoroughly addresses the foundational challenges inherent in scaling neural network training across cloud, cluster, and high-performance computing environments, presenting both the formal models and practical paradigms that underpin efficient parallelism. The text offers an in-depth technical journey into every critical component of the OneFlow architecture—from scheduling, resource management, and data pipelines to elasticity and fault recovery. Readers will find rigorous coverage of parallelism techniques, encompassing data, model, and pipeline parallelism, hybrid strategies, as well as device placement and load balancing for optimal efficiency. With advanced sections dedicated to state-of-the-art communication protocols, synchronization models, and hardware-aware optimizations, the book equips practitioners to maximize throughput and resilience in both research and production environments. Beyond architectural mastery, this book bridges theory with practice through hands-on guidance in cluster deployment, monitoring, security, debugging, and extensibility for heterogeneous backends. Case studies illuminate end-to-end applications in vision, NLP, and multimodal domains, while sections on federated learning, green AI, and compiler integration reveal emerging frontiers. Culminating with community-driven innovations and lessons from real-world deployments, this volume is an essential resource for engineers, researchers, and technical leaders seeking to harness the full potential of scalable, distributed deep learning with OneFlow.
Network And Parallel Computing
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Author : Shaoshan Liu
language : en
Publisher: Springer Nature
Release Date : 2022-11-30
Network And Parallel Computing written by Shaoshan Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-30 with Computers categories.
This book constitutes the proceedings of the 19th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2022, which was held in Jinan, China, during September 24-25, 2022. The 23 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 89 submissions. They were organized in topical sections as follows: computer architecture; cloud computing; deep learning; emerging applications; and storage and IO.
Network And Parallel Computing
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Author : Xu Chen
language : en
Publisher: Springer Nature
Release Date : 2025-03-28
Network And Parallel Computing written by Xu Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-28 with Computers categories.
This two part LNCS 15227 and 15528 volumes constitutes the proceedings of the 20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024, which was held in Haikou, China, during December 7–8, 2024. The 76 full papers presented in this volume were carefully reviewed and selected from 200 submissions. They are organized according to the following topics: Part-I : High-performance and Parallel Computing; Novel Memory and Storage Systems; and Emerging Architectures and Systems. Part-II : Edge Computing and Intelligence; Federated Learning Algorithms and Systems; Emerging Networks; and In-network Computing and Processing.
Job Scheduling Strategies For Parallel Processing
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Author : Dalibor Klusáček
language : en
Publisher: Springer Nature
Release Date : 2024-12-20
Job Scheduling Strategies For Parallel Processing written by Dalibor Klusáček and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-20 with Computers categories.
This book constitutes the refereed proceedings of the 27th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2024, held in San Francisco, CA, USA, on May 31, 2024. The 10 full papers included in this book were carefully reviewed and selected from 15 submissions. The JSSPP 2024 covers several interesting problems within the resource management and scheduling domains.
Speech And Speaker Recognition
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Author : Manfred Robert Schroeder
language : en
Publisher: Karger Medical and Scientific Publishers
Release Date : 1985-01-01
Speech And Speaker Recognition written by Manfred Robert Schroeder and has been published by Karger Medical and Scientific Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 1985-01-01 with Medical categories.
Mastering Machine Learning For Penetration Testing
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Author : Chiheb Chebbi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-06-27
Mastering Machine Learning For Penetration Testing written by Chiheb Chebbi and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-27 with Language Arts & Disciplines categories.
Become a master at penetration testing using machine learning with Python Key Features Identify ambiguities and breach intelligent security systems Perform unique cyber attacks to breach robust systems Learn to leverage machine learning algorithms Book Description Cyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it’s important for pentesters and security researchers to understand how these systems work, and to breach them for testing purposes. This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you’ve gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you’ll see how to find loopholes and surpass a self-learning security system. As you make your way through the chapters, you’ll focus on topics such as network intrusion detection and AV and IDS evasion. We’ll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system. What you will learn Take an in-depth look at machine learning Get to know natural language processing (NLP) Understand malware feature engineering Build generative adversarial networks using Python libraries Work on threat hunting with machine learning and the ELK stack Explore the best practices for machine learning Who this book is for This book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system. Basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary.
Big Data Analytics With Java
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Author : Rajat Mehta
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-07-31
Big Data Analytics With Java written by Rajat Mehta and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-31 with Computers categories.
Learn the basics of analytics on big data using Java, machine learning and other big data tools About This Book Acquire real-world set of tools for building enterprise level data science applications Surpasses the barrier of other languages in data science and learn create useful object-oriented codes Extensive use of Java compliant big data tools like apache spark, Hadoop, etc. Who This Book Is For This book is for Java developers who are looking to perform data analysis in production environment. Those who wish to implement data analysis in their Big data applications will find this book helpful. What You Will Learn Start from simple analytic tasks on big data Get into more complex tasks with predictive analytics on big data using machine learning Learn real time analytic tasks Understand the concepts with examples and case studies Prepare and refine data for analysis Create charts in order to understand the data See various real-world datasets In Detail This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset. This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naive Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world. Style and approach The approach of book is to deliver practical learning modules in manageable content. Each chapter is a self-contained unit of a concept in big data analytics. Book will step by step builds the competency in the area of big data analytics. Examples using real world case studies to give ideas of real applications and how to use the techniques mentioned. The examples and case studies will be shown using both theory and code.
Probabilistic Deep Learning
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Author : Oliver Duerr
language : en
Publisher: Manning
Release Date : 2020-11-10
Probabilistic Deep Learning written by Oliver Duerr and has been published by Manning this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-10 with Computers categories.
Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications. Summary Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability teaches the increasingly popular probabilistic approach to deep learning that allows you to refine your results more quickly and accurately without much trial-and-error testing. Emphasizing practical techniques that use the Python-based Tensorflow Probability Framework, you’ll learn to build highly-performant deep learning applications that can reliably handle the noise and uncertainty of real-world data. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology The world is a noisy and uncertain place. Probabilistic deep learning models capture that noise and uncertainty, pulling it into real-world scenarios. Crucial for self-driving cars and scientific testing, these techniques help deep learning engineers assess the accuracy of their results, spot errors, and improve their understanding of how algorithms work. About the book Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications. What's inside Explore maximum likelihood and the statistical basis of deep learning Discover probabilistic models that can indicate possible outcomes Learn to use normalizing flows for modeling and generating complex distributions Use Bayesian neural networks to access the uncertainty in the model About the reader For experienced machine learning developers. About the author Oliver Dürr is a professor at the University of Applied Sciences in Konstanz, Germany. Beate Sick holds a chair for applied statistics at ZHAW and works as a researcher and lecturer at the University of Zurich. Elvis Murina is a data scientist. Table of Contents PART 1 - BASICS OF DEEP LEARNING 1 Introduction to probabilistic deep learning 2 Neural network architectures 3 Principles of curve fitting PART 2 - MAXIMUM LIKELIHOOD APPROACHES FOR PROBABILISTIC DL MODELS 4 Building loss functions with the likelihood approach 5 Probabilistic deep learning models with TensorFlow Probability 6 Probabilistic deep learning models in the wild PART 3 - BAYESIAN APPROACHES FOR PROBABILISTIC DL MODELS 7 Bayesian learning 8 Bayesian neural networks
Co Governed Sovereignty Network
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Author : Hui Li
language : en
Publisher: Springer Nature
Release Date : 2021-07-26
Co Governed Sovereignty Network written by Hui Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-26 with Computers categories.
This open access book introduces MIN, a novel networking architecture to implement the sovereign equality of all countries in the cyberspace. Combining legal theory and network technology, it first discusses the historical development of sovereignty and expounds the legal basis of cyberspace sovereignty. Then, based on the high-performance blockchain, it describes a new network architecture designed to implement co-governance at the technical level. Explaining network sovereignty and including rich illustrations and tables, the book helps readers new to the field grasp the evolution and necessity of cyberspace sovereignty, gain insights into network trends and develop a preliminary understanding of complex network technologies such as blockchain, security mechanisms and routing strategies. The MIN network implements the “four principles” of cyberspace adopted by most nations and people: respecting cyber sovereignty; maintaining peace and protection; promoting opennessand cooperation; and building good order to provide network system security. There maybe three scales of application scenario for MIN, the big one is for UN of Cyberspace, the middle one is for Smart city, the small one is for enterprise group or organizations as private network, MIN-VPN. We have developed the product of MIN-VPN, you could find its message on the preface if care about the security of your network.
Knowledge Discovery From Data Streams
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Author : Joao Gama
language : en
Publisher: CRC Press
Release Date : 2010-05-25
Knowledge Discovery From Data Streams written by Joao Gama and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-05-25 with Business & Economics categories.
Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents a coherent overview of state-of-the-art research in learning from data streams. The book covers the fundamentals that are imperative to understanding data streams and describes important applications, such as TCP/IP traffic, GPS data, sensor networks, and customer click streams. It also addresses several challenges of data mining in the future, when stream mining will be at the core of many applications. These challenges involve designing useful and efficient data mining solutions applicable to real-world problems. In the appendix, the author includes examples of publicly available software and online data sets. This practical, up-to-date book focuses on the new requirements of the next generation of data mining. Although the concepts presented in the text are mainly about data streams, they also are valid for different areas of machine learning and data mining.