rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Algorithms with JULIA
    sub title: Optimization, Machine Learning, and Differential Equations Using the JULIA Language
  name:
    name part: Heitzinger, Clemens.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2022
    edition: 1st ed. 2022.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XXI, 439 pages 15 illustrations, 13 illustrations in color)
  abstract: This book provides an introduction to modern topics in scientific computing and machine learning, using JULIA to illustrate the efficient implementation of algorithms. In addition to covering fundamental topics, such as optimization and solving systems of equations, it adds to the usual canon of computational science by including more advanced topics of practical importance. In particular, there is a focus on partial differential equations and systems thereof, which form the basis of many engineering applications. Several chapters also include material on machine learning (artificial neural networks and Bayesian estimation). JULIA is a relatively new programming language which has been developed with scientific and technical computing in mind. Its syntax is similar to other languages in this area, but it has been designed to embrace modern programming concepts. It is open source, and it comes with a compiler and an easy-to-use package system. Aimed at students of applied mathematics, computer science, engineering and bioinformatics, the book assumes only a basic knowledge of linear algebra and programming.
  table of contents: An Introduction to the Julia Language -- Functions -- Variables, Constants, Scopes, and Modules -- Built-in Data Structures -- User Defined Data Structures and the Type System -- Control Flow -- Macros -- Arrays and Linear Algebra -- Ordinary Differential Equations -- Partial-Differential Equations -- Global Optimization -- Local Optimization -- Neural Networks -- Bayesian Estimation.
  note: by Clemens Heitzinger.
  subject:
    topic: Numerical analysis
  subject:
    topic: Mathematical analysis
  subject:
    topic: Numerical Analysis
  subject:
    topic: Analysis
  classification: QA297-299.4
  classification: 518
  related item:
    title info:
      title: Springer Nature eBook
  identifier: 9978-3-031-16560-3
  identifier: 9783031165603
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 221212
    record change date: 20240805181812.4
    record identifier: in00004659900