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Algorithms with JULIA = optimization...

Heitzinger, Clemens.

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Algorithms with JULIA = optimization, machine learning, and differential Equations using the JULIA language /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Algorithms with JULIA/ by Clemens Heitzinger.
Reminder of title:	optimization, machine learning, and differential Equations using the JULIA language /
Author:	Heitzinger, Clemens.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xxi, 439 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Computer algorithms. -
Online resource:	https://doi.org/10.1007/978-3-031-16560-3
ISBN:	9783031165603

Algorithms with JULIA = optimization, machine learning, and differential Equations using the JULIA language /
Heitzinger, Clemens.

Algorithms with JULIAoptimization, machine learning, and differential Equations using the JULIA language /[electronic resource] :by Clemens Heitzinger. - Cham :Springer International Publishing :2022. - xxi, 439 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9783031165603

Standard No.: 10.1007/978-3-031-16560-3doiSubjects--Topical Terms:

523872
Computer algorithms.

LC Class. No.: QA76.9.A43

Dewey Class. No.: 005.13

Algorithms with JULIA = optimization, machine learning, and differential Equations using the JULIA language /
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245 1 0 $a Algorithms with JULIA $h [electronic resource] : $b optimization, machine learning, and differential Equations using the JULIA language / $c by Clemens Heitzinger.
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300 $a xxi, 439 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
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