CURRENTLY SOLD OUT
About this product
Product Identifiers
PublisherSpringer International Publishing A&G
ISBN-103031165594
ISBN-139783031165597
eBay Product ID (ePID)24057261271
Product Key Features
Number of PagesXxi, 439 Pages
LanguageEnglish
Publication NameAlgorithms with JULIA : Optimization, Machine Learning, and Differential Equations Using the JULIA Language
SubjectNumerical Analysis, General, Mathematical Analysis
Publication Year2022
TypeTextbook
AuthorClemens Heitzinger
Subject AreaMathematics
FormatHardcover
Dimensions
Item Weight33.2 Oz
Item Length9.3 in
Item Width6.1 in
Additional Product Features
Reviews"The author's writing style is clear and concise, making the book easy to follow and understand. The book also includes useful code snippets and diagrams that help illustrate the concepts and algorithms discussed. ... the book is well-written and an excellent resource for all those interested in learning the Julia language along with its applications. The extensive discussion of algorithms covering a variety of topics makes it a beneficial book for students, teachers, and researchers alike." (Syed Inayatullah, zbMATH 1512.90003, 2023)
Dewey Edition23
Number of Volumes1 vol.
IllustratedYes
Dewey Decimal519.7
Table Of ContentAn 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.
SynopsisIncludes an in-depth discussion of Julia for the purposes of scientific/technical computing as an open-source alternative for MATLAB One of the first books about scientific/technical computing using Julia at a non-trivial application level Written at an introductory level Focuses on applications which may be a bit more advanced than what is typically found in books on scientific computing, but which are immediately useful nowadays. Enables the reader to solve more modern, complicated problems, 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 ofapplied mathematics, computer science, engineering and bioinformatics, the book assumes only a basic knowledge of linear algebra and programming.
LC Classification NumberQA297-299.4
