LEADER 03990nam 22004935 450 001 9910921008103321 005 20250504110110.0 010 $a9798868809651 024 7 $a10.1007/979-8-8688-0965-1 035 $a(CKB)37156201300041 035 $a(MiAaPQ)EBC31876706 035 $a(Au-PeEL)EBL31876706 035 $a(DE-He213)979-8-8688-0965-1 035 $a(CaSebORM)9798868809651 035 $a(OCoLC)1487664583 035 $a(OCoLC-P)1487664583 035 $a(EXLCZ)9937156201300041 100 $a20250104d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aJulia Quick Syntax Reference $eA Pocket Guide for Data Science Programming /$fby Antonello Lobianco 205 $a2nd ed. 2024. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2024. 215 $a1 online resource (239 pages) 300 $aIncludes index. 311 08$a9798868809644 327 $aPart 1. Language Core -- 1. Getting Started -- 2. Data Types and Structures -- 3. Control Flow and Functions -- 4. Custom Types -- E1: Shelling Segregation Model - 5. Input ? Output -- 6. Metaprogramming and Macros -- 7. Interfacing Julia with Other Languages -- 8. Efficiently Write Efficient Code. - 9 Parallel Computing in Julia - Part 2. Packages Ecosystem -- 10. Working with Data -- 11. Scientific Libraries -- E2: Fitting a forest growth model - 12 ? AI with Julia ? E3. Predict house values - 13. Utilities. Appendix: Solutions to the exercises. 330 $aLearn the Julia programming language as quickly as possible. This book is a must-have reference guide that presents the essential Julia syntax in a well-organized format, updated with the latest features of Julia?s APIs, libraries, and packages. This book provides an introduction that reveals basic Julia structures and syntax; discusses data types, control flow, functions, input/output, exceptions, metaprogramming, performance, and more. Additionally, you'll learn to interface Julia with other programming languages such as R for statistics or Python. At a more applied level, you will learn how to use Julia packages for data analysis, numerical optimization, symbolic computation, and machine learning, and how to present your results in dynamic documents. The Second Edition delves deeper into modules, environments, and parallelism in Julia. It covers random numbers, reproducibility in stochastic computations, and adds a section on probabilistic analysis. Finally, it provides forward-thinking introductions to AI and machine learning workflows using BetaML, including regression, classification, clustering, and more, with practical exercises and solutions for self-learners. What You Will Learn Work with Julia types and the different containers for rapid development Use vectorized, classical loop-based code, logical operators, and blocks Explore Julia functions: arguments, return values, polymorphism, parameters, anonymous functions, and broadcasts Build custom structures in Julia Use C/C++, Python or R libraries in Julia and embed Julia in other code. Optimize performance with GPU programming, profiling and more. Manage, prepare, analyse and visualise your data with DataFrames and Plots Implement complete ML workflows with BetaML, from data coding to model evaluation, and more. Who This Book Is For Experienced programmers who are new to Julia, as well as data scientists who want to improve their analysis or try out machine learning algorithms with Julia. 606 $aJulia (Computer program language) 606 $aComputer programming 615 0$aJulia (Computer program language) 615 0$aComputer programming. 676 $a005.45 700 $aLobianco$b Antonello$01064804 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910921008103321 996 $aJulia Quick Syntax Reference$92541034 997 $aUNINA