Hugendubel.info - Die B2B Online-Buchhandlung 

Merkliste
Die Merkliste ist leer.
Bitte warten - die Druckansicht der Seite wird vorbereitet.
Der Druckdialog öffnet sich, sobald die Seite vollständig geladen wurde.
Sollte die Druckvorschau unvollständig sein, bitte schliessen und "Erneut drucken" wählen.

Architecture of Advanced Numerical Analysis Systems

Designing a Scientific Computing System using OCaml
BuchKartoniert, Paperback
472 Seiten
Englisch
Springererschienen am27.12.20221st ed.
This unique open access book applies the functional OCaml programming language to numerical or computational weighted data science, engineering, and scientific applications. This book is based on the authors' first-hand experience building and maintaining Owl, an OCaml-based numerical computing library. You'll first learn the various components in a modern numerical computation library. Then, you will learn how these components are designed and built up and how to optimize their performance. After reading and using this book, you'll have the knowledge required to design and build real-world complex systems that effectively leverage the advantages of the OCaml functional programming language. What You Will LearnOptimize core operations based on N-dimensional arraysDesign and implement an industry-level algorithmic differentiation moduleImplement mathematical optimization, regression, and deep neural network functionalities based on algorithmic differentiationDesign and optimize a computation graph module, and understand the benefits it brings to the numerical computing libraryAccommodate the growing number of hardware accelerators (e.g. GPU, TPU) and execution backends (e.g. web browser, unikernel) of numerical computationUse the Zoo system for efficient scripting, code sharing, service deployment, and compositionDesign and implement a distributed computing engine to work with a numerical computing library, providing convenient APIs and high performance Who This Book Is For Those with prior programming experience, especially with the OCaml programming language, or with scientific computing experience who may be new to OCaml. Most importantly, it is for those who are eager to understand not only how to use something, but also how it is built up.mehr

Produkt

KlappentextThis unique open access book applies the functional OCaml programming language to numerical or computational weighted data science, engineering, and scientific applications. This book is based on the authors' first-hand experience building and maintaining Owl, an OCaml-based numerical computing library. You'll first learn the various components in a modern numerical computation library. Then, you will learn how these components are designed and built up and how to optimize their performance. After reading and using this book, you'll have the knowledge required to design and build real-world complex systems that effectively leverage the advantages of the OCaml functional programming language. What You Will LearnOptimize core operations based on N-dimensional arraysDesign and implement an industry-level algorithmic differentiation moduleImplement mathematical optimization, regression, and deep neural network functionalities based on algorithmic differentiationDesign and optimize a computation graph module, and understand the benefits it brings to the numerical computing libraryAccommodate the growing number of hardware accelerators (e.g. GPU, TPU) and execution backends (e.g. web browser, unikernel) of numerical computationUse the Zoo system for efficient scripting, code sharing, service deployment, and compositionDesign and implement a distributed computing engine to work with a numerical computing library, providing convenient APIs and high performance Who This Book Is For Those with prior programming experience, especially with the OCaml programming language, or with scientific computing experience who may be new to OCaml. Most importantly, it is for those who are eager to understand not only how to use something, but also how it is built up.
Details
ISBN/GTIN978-1-4842-8852-8
ProduktartBuch
EinbandartKartoniert, Paperback
Verlag
Erscheinungsjahr2022
Erscheinungsdatum27.12.2022
Auflage1st ed.
Seiten472 Seiten
SpracheEnglisch
IllustrationenXIII, 472 p. 57 illus., 43 illus. in color.
Artikel-Nr.51021726

Inhalt/Kritik

Inhaltsverzeichnis
Chapter 1: Introduction.-Chapter 2: Core Optimization.- Chapter 3: Algorithm Differentiation.- Chapter 4: Mathematical Optimization.- Chapter 5: Deep Neural Networks.- Chapter 6: Computation Graph.- Chapter 7: Performance Accelerators.- Chapter 8: Compiler Backends.- Chapter 9: Composition and Deployment.- Chapter 10: Distributed Computing.- Chapter 11: Testing Framework.- Appendix A: Basic Analytics Examples.- Appendix B: System Conventions.- Appendix C: Metric Systems and Constants.- Appendix D: AlgoDiff Module.- Appendix E: Neural Network Module.- Appendix F: Actor System for Distributed Computing.- Bibliography.mehr

Autor

Liang Wang is the Chief AI Architect at Nokia, the Chief Scientific Officer at iKVA, a Senior Researcher at the University of Cambridge, and an Intel Software Innovator. He has a broad research interest in artificial intelligence, machine learning, operating systems, computer networks, optimization theory, and graph theory.

Jianxin Zhao is a PhD graduate from the University of Cambridge, supervised by Prof. Jon Crowcroft. His research interests include numerical computation, high-performance computing, machine learning, and their application in the real world.