Algorithms with JULIA : optimization, machine learning, and differential Equations using the JULIA language / Clemens Heitzinger.
Heitzinger, Clemens.
2022
QA76.9.A43
Available Online

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Title Algorithms with JULIA : optimization, machine learning, and differential Equations using the JULIA language / Clemens Heitzinger.
Author Heitzinger, Clemens.
ISBN 9783031165603 (electronic bk.)
3031165608 (electronic bk.)
3031165594
9783031165597
DOI https://doi.org/10.1007/978-3-031-16560-3
Imprint Cham : Springer, 2022.
Language English
Description 1 online resource (447 p.)
Other Standard Identifiers 10.1007/978-3-031-16560-3 doi
Call Number QA76.9.A43
Dewey Decimal Classification 005.13
Summary 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.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed January 9, 2023).
Available in Other Form Algorithms with JULIA
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Record Appears in Online Resources > Ebooks
All Resources
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.

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