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024 7 _a10.1007/979-8-8688-0965-1
_2doi
050 4 _aQA76.76.C65
072 7 _aUMC
_2bicssc
072 7 _aCOM010000
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072 7 _aUMC
_2thema
100 1 _aLobianco, Antonello.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_990163
245 1 0 _aJulia Quick Syntax Reference
_h[electronic resource] :
_bA Pocket Guide for Data Science Programming /
_cby Antonello Lobianco.
250 _a2nd ed. 2024.
264 1 _aBerkeley, CA :
_bApress :
_bImprint: Apress,
_c2024.
300 _aXV, 361 p. 83 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _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.
520 _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.
650 0 _aCompilers (Computer programs).
_966172
650 0 _aArtificial intelligence.
_958441
650 0 _aData mining.
_96941
650 0 _aComputer science
_xMathematics.
_964041
650 1 4 _aCompilers and Interpreters.
_966173
650 2 4 _aArtificial Intelligence.
_958441
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aMathematics of Computing.
_966501
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9798868809644
776 0 8 _iPrinted edition:
_z9798868809668
856 4 0 _uhttps://doi.org/10.1007/979-8-8688-0965-1
912 _aZDB-2-CWD
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