LEADER 03863nam 2200433 450 001 9910808268503321 005 20201001133238.5 010 $a1-83882-467-7 035 $a(CKB)4100000008335300 035 $a(MiAaPQ)EBC5778836 035 $a(CaSebORM)9781838822248 035 $a(PPN)238420361 035 $a(EXLCZ)994100000008335300 100 $a20190607d2019 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aJulia 1. 0 programming complete reference guide $ediscover julia, a high-performance language for technical computing /$fIvo Balbaert, Adrian Salceanu 205 $a1st edition 210 1$aBirmingham ;$aMumbai :$cPackt,$d2019. 215 $a1 online resource (441 pages) 311 $a1-83882-224-0 330 $aLearn dynamic programming with Julia to build apps for data analysis, visualization, machine learning, and the web Key Features Leverage Julia's high speed and efficiency to build fast, efficient applications Perform supervised and unsupervised machine learning and time series analysis Tackle problems concurrently and in a distributed environment Book Description Julia offers the high productivity and ease of use of Python and R with the lightning-fast speed of C++. There's never been a better time to learn this language, thanks to its large-scale adoption across a wide range of domains, including fintech, biotech and artificial intelligence (AI). You will begin by learning how to set up a running Julia platform, before exploring its various built-in types. This Learning Path walks you through two important collection types: arrays and matrices. You'll be taken through how type conversions and promotions work, and in further chapters you'll study how Julia interacts with operating systems and other languages. You'll also learn about the use of macros, what makes Julia suitable for numerical and scientific computing, and how to run external programs. Once you have grasped the basics, this Learning Path goes on to how to analyze the Iris dataset using DataFrames. While building a web scraper and a web app, you'll explore the use of functions, methods, and multiple dispatches. In the final chapters, you'll delve into machine learning, where you'll build a book recommender system. By the end of this Learning Path, you'll be well versed with Julia and have the skills you need to leverage its high speed and efficiency for your applications. This Learning Path includes content from the following Packt products: Julia 1.0 Programming - Second Edition by Ivo Balbaert Julia Programming Projects by Adrian Salceanu What you will learn Create your own types to extend the built-in type system Visualize your data in Julia with plotting packages Explore the use of built-in macros for testing and debugging Integrate Julia with other languages such as C, Python, and MATLAB Analyze and manipulate datasets using Julia and DataFrames Develop and run a web app using Julia and the HTTP package Build a recommendation system using supervised machine learning Who this book is for If you are a statistician or data scientist who wants a quick course in the Julia programming language while building big data applications, this Learning Path is for you. Basic knowledge of m... 606 $aProgramming languages (Electronic computers) 606 $aApplication software$xDevelopment 615 0$aProgramming languages (Electronic computers) 615 0$aApplication software$xDevelopment. 676 $a005.13 700 $aBalbaert$b Ivo$0898566 702 $aSalceanu$b Adrian 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910808268503321 996 $aJulia 1. 0 programming complete reference guide$94101244 997 $aUNINA