High-Performance Tensor Computations in Scientific Computing and Data Science
Author: Edoardo Angelo Di Napoli
Publisher: Frontiers Media SA
Published: 2022-11-08
Total Pages: 192
ISBN-13: 2832504256
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User-Defined Tensor Data Analysis
Author: Bin Dong
Publisher: Springer Nature
Published: 2021-09-29
Total Pages: 111
ISBN-13: 3030707504
DOWNLOAD EBOOKThe SpringerBrief introduces FasTensor, a powerful parallel data programming model developed for big data applications. This book also provides a user's guide for installing and using FasTensor. FasTensor enables users to easily express many data analysis operations, which may come from neural networks, scientific computing, or queries from traditional database management systems (DBMS). FasTensor frees users from all underlying and tedious data management tasks, such as data partitioning, communication, and parallel execution. This SpringerBrief gives a high-level overview of the state-of-the-art in parallel data programming model and a motivation for the design of FasTensor. It illustrates the FasTensor application programming interface (API) with an abundance of examples and two real use cases from cutting edge scientific applications. FasTensor can achieve multiple orders of magnitude speedup over Spark and other peer systems in executing big data analysis operations. FasTensor makes programming for data analysis operations at large scale on supercomputers as productively and efficiently as possible. A complete reference of FasTensor includes its theoretical foundations, C++ implementation, and usage in applications. Scientists in domains such as physical and geosciences, who analyze large amounts of data will want to purchase this SpringerBrief. Data engineers who design and develop data analysis software and data scientists, and who use Spark or TensorFlow to perform data analyses, such as training a deep neural network will also find this SpringerBrief useful as a reference tool.
High Performance Computing for Computational Science – VECPAR 2018
Author: Hermes Senger
Publisher: Springer
Published: 2019-03-25
Total Pages: 272
ISBN-13: 3030159965
DOWNLOAD EBOOKThis book constitutes the thoroughly refereed post-conference proceedings of the 13th International Conference on High Performance Computing in Computational Science, VECPAR 2018, held in São Pedro, Brazil, in September 2018. The 17 full papers and one short paper included in this book were carefully reviewed and selected from 32 submissions presented at the conference. The papers cover the following topics: heterogeneous systems, shared memory systems and GPUs, and techniques including domain decomposition, scheduling and load balancing, with a strong focus on computational science applications.
Large-Scale Scientific Computing
Author: Ivan Lirkov
Publisher: Springer Nature
Published: 2020-02-13
Total Pages: 636
ISBN-13: 3030410323
DOWNLOAD EBOOKThis book constitutes revised papers from the 12th International Conference on Large-Scale Scientific Computing, LSSC 2019, held in Sozopol, Bulgaria, in June 2019. The 70 papers presented in this volume were carefully reviewed and selected from 81 submissions. The book also contains two invited talks. The papers were organized in topical sections named as follows: control and optimization of dynamical systems; meshfree and particle methods; fractional diffusion problems: numerical methods, algorithms and applications; pore scale flow and transport simulation; tensors based algorithms and structures in optimization and applications; HPC and big data: algorithms and applications; large-scale models: numerical methods, parallel computations and applications; monte carlo algorithms: innovative applications in conjunctions with other methods; application of metaheuristics to large-scale problems; large scale machine learning: multiscale algorithms and performance guarantees; and contributed papers.
Computational Statistics in Data Science
Author: Richard A. Levine
Publisher: John Wiley & Sons
Published: 2022-03-23
Total Pages: 672
ISBN-13: 1119561086
DOWNLOAD EBOOKEin unverzichtbarer Leitfaden bei der Anwendung computergestützter Statistik in der modernen Datenwissenschaft In Computational Statistics in Data Science präsentiert ein Team aus bekannten Mathematikern und Statistikern eine fundierte Zusammenstellung von Konzepten, Theorien, Techniken und Praktiken der computergestützten Statistik für ein Publikum, das auf der Suche nach einem einzigen, umfassenden Referenzwerk für Statistik in der modernen Datenwissenschaft ist. Das Buch enthält etliche Kapitel zu den wesentlichen konkreten Bereichen der computergestützten Statistik, in denen modernste Techniken zeitgemäß und verständlich dargestellt werden. Darüber hinaus bietet Computational Statistics in Data Science einen kostenlosen Zugang zu den fertigen Einträgen im Online-Nachschlagewerk Wiley StatsRef: Statistics Reference Online. Außerdem erhalten die Leserinnen und Leser: * Eine gründliche Einführung in die computergestützte Statistik mit relevanten und verständlichen Informationen für Anwender und Forscher in verschiedenen datenintensiven Bereichen * Umfassende Erläuterungen zu aktuellen Themen in der Statistik, darunter Big Data, Datenstromverarbeitung, quantitative Visualisierung und Deep Learning Das Werk eignet sich perfekt für Forscher und Wissenschaftler sämtlicher Fachbereiche, die Techniken der computergestützten Statistik auf einem gehobenen oder fortgeschrittenen Niveau anwenden müssen. Zudem gehört Computational Statistics in Data Science in das Bücherregal von Wissenschaftlern, die sich mit der Erforschung und Entwicklung von Techniken der computergestützten Statistik und statistischen Grafiken beschäftigen.
Intelligent Computing
Author: Kohei Arai
Publisher: Springer Nature
Published: 2023-10-02
Total Pages: 1492
ISBN-13: 3031377176
DOWNLOAD EBOOKThis book is a collection of insightful and unique state-of the-art papers presented at the Computing Conference which took place in London on June 22–23, 2023. A total of 539 papers were received out of which 193 were selected for presenting after double-blind peer-review. The book covers a wide range of scientific topics including IoT, Artificial Intelligence, Computing, Data Science, Networking, Data security and Privacy, etc. The conference was successful in reaping the advantages of both online and offline modes. The goal of this conference is to give a platform to researchers with fundamental contributions and to be a premier venue for academic and industry practitioners to share new ideas and development experiences. We hope that readers find this book interesting and valuable. We also expect that the conference and its publications will be a trigger for further related research and technology improvements in this important subject.
Convergence: Artificial Intelligence and Quantum Computing
Author: Greg Viggiano
Publisher: John Wiley & Sons
Published: 2022-11-03
Total Pages: 210
ISBN-13: 139417411X
DOWNLOAD EBOOKPrepare for the coming convergence of AI and quantum computing A collection of essays from 20 renowned, international authors working in industry, academia, and government, Convergence: Artificial Intelligence and Quantum Computing explains the impending convergence of artificial intelligence and quantum computing. A diversity of viewpoints is presented, each offering their view of this coming watershed event. In the book, you’ll discover that we’re on the cusp of seeing the stuff of science fiction become reality, with huge implications for ripping up the existing social fabric, global economy, and current geopolitical order. Along with an incisive foreword by Hugo- and Nebula-award winning author David Brin, you’ll also find: Explorations of the increasing pace of technological development Explanations of why seemingly unusual and surprising breakthroughs might be just around the corner Maps to navigate the potential minefields that await us as AI and quantum computing come together A fascinating and thought-provoking compilation of insights from some of the leading technological voices in the world, Convergence convincingly argues that we should prepare for a world in which very little will remain the same and shows us how to get ready.
Database Management using AI: A Comprehensive Guide
Author: A Purushotham Reddy
Publisher: A Purushotham Reddy
Published: 2024-10-20
Total Pages: 2066
ISBN-13:
DOWNLOAD EBOOKDatabase Management Using AI: A Comprehensive Guide is a professional yet accessible exploration of how artificial intelligence (AI) is reshaping the world of database management. Designed for database administrators, data scientists, and tech enthusiasts, this book walks readers through the transformative impact of AI on modern data systems. The guide begins with the fundamentals of database management, covering key concepts such as data models, SQL, and the principles of database design. From there, it delves into the powerful role AI plays in optimizing database performance, enhancing security, and automating complex tasks like data retrieval, query optimization, and schema design. The book doesn't stop at theory. It brings AI to life with practical case studies showing how AI-driven database systems are being used in industries such as e-commerce, healthcare, finance, and logistics. These real-world examples demonstrate AI's role in improving efficiency, reducing errors, and driving intelligent decision-making. Key topics covered include: Introduction to Database Systems: Fundamentals of database management, from relational databases to modern NoSQL systems. AI Integration: How AI enhances database performance, automates routine tasks, and strengthens security. Real-World Applications: Case studies from diverse sectors like healthcare, finance, and retail, showcasing the practical impact of AI in database management. Predictive Analytics and Data Mining: How AI tools leverage data to make accurate predictions and uncover trends. Future Trends: Explore cutting-edge innovations like autonomous databases and cloud-based AI solutions that are shaping the future of data management. With its clear explanations and actionable insights, Database Management Using AI equips readers with the knowledge to navigate the fast-evolving landscape of AI-powered databases, making it a must-have resource for those looking to stay ahead in the digital age.
Scientific Computing with Python - Second Edition
Author: CLAUS. FUHRER
Publisher:
Published: 2021-07-23
Total Pages: 392
ISBN-13: 9781838822323
DOWNLOAD EBOOKLeverage this example-packed, comprehensive guide for all your Python computational needs Key Features: Learn the first steps within Python to highly specialized concepts Explore examples and code snippets taken from typical programming situations within scientific computing. Delve into essential computer science concepts like iterating, object-oriented programming, testing, and MPI presented in strong connection to applications within scientific computing. Book Description: Python has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python. This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations. By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing. What You Will Learn: Understand the building blocks of computational mathematics, linear algebra, and related Python objects Use Matplotlib to create high-quality figures and graphics to draw and visualize results Apply object-oriented programming (OOP) to scientific computing in Python Discover how to use pandas to enter the world of data processing Handle exceptions for writing reliable and usable code Cover manual and automatic aspects of testing for scientific programming Get to grips with parallel computing to increase computation speed Who this book is for: This book is for students with a mathematical background, university teachers designing modern courses in programming, data scientists, researchers, developers, and anyone who wants to perform scientific computation in Python.
Recommender Systems
Author: Dietmar Jannach
Publisher: Cambridge University Press
Published: 2010-09-30
Total Pages:
ISBN-13: 1139492594
DOWNLOAD EBOOKIn this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.
