Large-Scale Graph Analysis: System, Algorithm and Optimization, Shao Yingxia, Cui Bin, Chen Lei
Автор: Steven L. Brunton, J. Nathan Kutz Название: Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control ISBN: 1108422098 ISBN-13(EAN): 9781108422093 Издательство: Amazon Internet Рейтинг: Цена: 0.00 T Наличие на складе: Невозможна поставка. Описание: Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. Aimed at advanced undergraduate and beginning graduate students, this textbook provides an integrated viewpoint that shows how to apply emerging methods from data science, data mining, and machine learning to engineering and the physical sciences.
Автор: Ravindra K. Ahuja; Rolf H. M?hring; Christos Zarol Название: Robust and Online Large-Scale Optimization ISBN: 3642054641 ISBN-13(EAN): 9783642054648 Издательство: Springer Рейтинг: Цена: 83850.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Scheduled transportation networks give rise to complex and large-scale network optimization problems requiring innovative solution techniques and ideas from mathematical optimization and theoretical computer science. This book features papers on this topic.
Автор: Subhendu Bikash Hazra Название: Large-Scale PDE-Constrained Optimization in Applications ISBN: 3642015018 ISBN-13(EAN): 9783642015014 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: With continuous development of modern computing hardware and applicable - merical methods, computational ?uid dynamics (CFD) has reached certain level of maturity so that it is being used routinely by scientists and engineers for ?uid ?ow analysis.
Автор: Subhendu Bikash Hazra Название: Large-Scale PDE-Constrained Optimization in Applications ISBN: 3642263887 ISBN-13(EAN): 9783642263880 Издательство: Springer Рейтинг: Цена: 130430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book develops mathematical methods and algorithms that lead to efficient and high performance computational techniques to solve simulation based optimization problems in real-life applications.
Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies
Key Features:
Get up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choice
Eliminate mundane tasks in data engineering and reduce human errors in machine learning models
Find out how you can make machine learning accessible for all users to promote decentralized processes
Book Description:
Every machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort.
This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and much more. You'll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing AutoML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you'll explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle.
By the end of this machine learning book, you'll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks.
What You Will Learn:
Explore AutoML fundamentals, underlying methods, and techniques
Assess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenario
Find out the difference between cloud and operations support systems (OSS)
Implement AutoML in enterprise cloud to deploy ML models and pipelines
Build explainable AutoML pipelines with transparency
Understand automated feature engineering and time series forecasting
Automate data science modeling tasks to implement ML solutions easily and focus on more complex problems
Who this book is for:
Citizen data scientists, machine learning developers, artificial intelligence enthusiasts, or anyone looking to automatically build machine learning models using the features offered by open source tools, Microsoft Azure Machine Learning, AWS, and Google Cloud Platform will find this book useful. Beginner-level knowledge of building ML models is required to get the best out of this book. Prior experience in using Enterprise cloud is beneficial.
1 Classification of Low Dimensional 3-Lie Superalgebras: V. Abramov and P. Lдtt.- 2 Semi-Commutative Galois Extension and Reduced Quantum Plane: V. Abramov and Md. Raknuzzaman.- 3 Valued Custom Skew Fields with Generalised PBW Property from Power Series Construction: L. Hellstrцm.- 4 Computing Burchnall-Chaundy Polynomials with Determinants: J. Richter and S. Silvestrov.- 5 Centralizers and Pseudo-Degree Functions: J. Richter.- 6 Asymptotic Expansions for Moment Functionals of Perturbed Discrete Time Semi-Markov Processes: M. Petersson.- 7 Asymptotics for Quasi-Stationary Distributions of Perturbed Discrete Time Semi-Markov Processes: M. Petersson.- 8 PageRank, a Look at Small Changes in a Line of Nodes and the Complete Graph: Ch. Engstrцm and S. Silvestrov.- 9 PageRank, Connecting a Line of Nodes with a Complete Graph: Ch. Engstrцm and S. Silvestrov.- 10 Output Rate Variation Problem: Some Heuristic Paradigms and Dynamic Programming: G.B. Thapa and S. Silvestrov.- 11 Lp-Boundedness of Two Singular Integral Operators of Convolution Type: J. Musonda and S. Kaijser.- 12 Crossed Product Algebras for Piece-Wise Constant Functions: A.B. Tumwesigye et al.- 13 Linear Classification of Data with Support Vector Machines and Generalized Support Vector Machines: Xiaomin Qi et al.- 14 Linear and Nonlinear Classifiers of Data with Support Vector Machines and Generalized Support Vector Machines: X. Qi et al.- 15 Common Fixed Points of Weakly Commuting Multivalued Mappings on a Domain of Sets Endowed with Directed Graph: S. Silvestrov and T. Nazir.- 16 Common Fixed Point Results for Family of Generalized Multivalued F-contraction Mappings in Ordered Metric Spaces: T. Nazir and S. Silvestrov.
Автор: Irinel Caprini Название: Functional Analysis and Optimization Methods in Hadron Physics ISBN: 3030189473 ISBN-13(EAN): 9783030189471 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book begins with a brief historical review of the early applications of standard dispersion relations in particle physics. It then presents the modern perspective within the Standard Model, emphasizing the relation of analyticity together with alternative tools applied to strong interactions, such as perturbative and lattice quantum chromodynamics (QCD), as well as chiral perturbation theory. The core of the book argues that, in order to improve the prediction of specific hadronic observables, it is often necessary to resort to methods of complex analysis more sophisticated than the simple Cauchy integral. Accordingly, a separate mathematical chapter is devoted to solving several functional analysis optimization problems. Their applications to physical amplitudes and form factors are discussed in the following chapters, which also demonstrate how to merge the analytic approach with statistical analysis tools. Given its scope, the book offers a valuable guide for researchers working in precision hadronic physics, as well as graduate students who are new to the field.
Автор: Panos M. Pardalos; Thomas F. Coleman; Petros Xanth Название: Optimization and Data Analysis in Biomedical Informatics ISBN: 1489999663 ISBN-13(EAN): 9781489999665 Издательство: Springer Рейтинг: Цена: 102480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This work is targeted to applied mathematicians, computer scientists, industrial engineers, and clinical scientists who are interested in exploring emerging and fascinating interdisciplinary topics of research.
Автор: Surafel Luleseged Tilahun, Jean Medard T. Ngnotchouye Название: Optimization Techniques for Problem Solving in Uncertainty ISBN: 1522550917 ISBN-13(EAN): 9781522550914 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 189420.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: When it comes to optimization techniques, in some cases, the available information from real models may not be enough to construct either a probability distribution or a membership function for problem solving. In such cases, there are various theories that can be used to quantify the uncertain aspects.Optimization Techniques for Problem Solving in Uncertainty is a scholarly reference resource that looks at uncertain aspects involved in different disciplines and applications. Featuring coverage on a wide range of topics including uncertain preference, fuzzy multilevel programming, and metaheuristic applications, this book is geared towards engineers, managers, researchers, and post-graduate students seeking emerging research in the field of optimization.
Автор: Sri Niwas Singh; Fushuan Wen; Monika Jain Название: Advances in System Optimization and Control ISBN: 9811344752 ISBN-13(EAN): 9789811344756 Издательство: Springer Рейтинг: Цена: 186330.00 T Наличие на складе: Нет в наличии. Описание: This book comprises select proceedings of the International Conference on Advancement in Energy, Drives, and Control. It covers frontier topics in optimization and control. It covers applications of optimization processes in areas such as computer architecture, communication systems, system optimization, signal processing, fluid dynamics and process control. This book is of use to researchers, professionals, and students from across engineering disciplines.
Название: Mastering Spark with R: The Complete Guide to Large-Scale Analysis and Modeling ISBN: 149204637X ISBN-13(EAN): 9781492046370 Издательство: Wiley Рейтинг: Цена: 47510.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Spark from R to tackle big data and big compute problems.
Автор: Pennington Diane Название: Social Tagging for Linking Data Across Environments ISBN: 1783303387 ISBN-13(EAN): 9781783303380 Издательство: Facet Рейтинг: Цена: 112640.00 T Наличие на складе: Нет в наличии. Описание: This book, representing researchers and practitioners across different information professions, will explore how social tags can link content across a variety of environments.
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