Видавець: 24
Ivo Balbaert, Adrian Salceanu
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
Bogumił Kamiński, Przemysław Szufel
Julia, with its dynamic nature and high-performance, provides comparatively minimal time for the development of computational models with easy-to-maintain computational code. This book will be your solution-based guide as it will take you through different programming aspects with Julia.Starting with the new features of Julia 1.0, each recipe addresses a specific problem, providing a solution and explaining how it works. You will work with the powerful Julia tools and data structures along with the most popular Julia packages. You will learn to create vectors, handle variables, and work with functions. You will be introduced to various recipes for numerical computing, distributed computing, and achieving high performance. You will see how to optimize data science programs with parallel computing and memory allocation. We will look into more advanced concepts such as metaprogramming and functional programming. Finally, you will learn how to tackle issues while working with databases and data processing, and will learn about on data science problems, data modeling, data analysis, data manipulation, parallel processing, and cloud computing with Julia.By the end of the book, you will have acquired the skills to work more effectively with your data
Ivo Balbaert
The release of Julia 1.0 is now ready to change the technical world by combining the high productivity and ease of use of Python and R with the lightning-fast speed of C++. Julia 1.0 programming gives you a head start in tackling your numerical and data problems. You will begin by learning how to set up a running Julia platform, before exploring its various built-in types. With the help of practical examples, this book walks you through two important collection types: arrays and matrices. In addition to this, you will be taken through how type conversions and promotions work. In the course of the book, you will be introduced to the homo-iconicity and metaprogramming concepts in Julia. You will understand how Julia provides different ways to interact with an operating system, as well as other languages, and then you'll discover what macros are. Once you have grasped the basics, you’ll study what makes Julia suitable for numerical and scientific computing, and learn about the features provided by Julia. By the end of this book, you will also have learned how to run external programs. This book covers all you need to know about Julia in order to leverage its high speed and efficiency for your applications.
Julia Cookbook. Click here to enter text
Jalem Raj Rohit
Want to handle everything that Julia can throw at you and get the most of it every day? This practical guide to programming with Julia for performing numerical computation will make you more productive and able work with data more efficiently. The book starts with the main features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We’ll also show you how to utilize the Julia language to identify, retrieve, and transform data sets so you can perform data analysis and data manipulation.Later on, you’ll see how to optimize data science programs with parallel computing and memory allocation. You’ll get familiar with the concepts of package development and networking to solve numerical problems using the Julia platform.This book includes recipes on identifying and classifying data science problems, data modelling, data analysis, data manipulation, meta-programming, multidimensional arrays, and parallel computing. By the end of the book, you will acquire the skills to work more effectively with your data.
Julia for Data Science. high-performance computing simplified
Anshul Joshi
Julia is a fast and high performing language that's perfectly suited to data science with a mature package ecosystem and is now feature complete. It is a good tool for a data science practitioner. There was a famous post at Harvard Business Review that Data Scientist is the sexiest job of the 21st century. (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century).This book will help you get familiarised with Julia's rich ecosystem, which is continuously evolving, allowing you to stay on top of your game.This book contains the essentials of data science and gives a high-level overview of advanced statistics and techniques. You will dive in and will work on generating insights by performing inferential statistics, and will reveal hidden patterns and trends using data mining. This has the practical coverage of statistics and machine learning. You will develop knowledge to build statistical models and machine learning systems in Julia with attractive visualizations.You will then delve into the world of Deep learning in Julia and will understand the framework, Mocha.jl with which you can create artificial neural networks and implement deep learning.This book addresses the challenges of real-world data science problems, including data cleaning, data preparation, inferential statistics, statistical modeling, building high-performance machine learning systems and creating effective visualizations using Julia.
Anna Legeżyńska
„Niewysoka, wyprostowana sylwetka. Skupione, uważne spojrzenie. Niezmienne uczesanie z charakterystyczną, stylową koroną warkocza wokół głowy. Zawsze ten sam sznureczek pereł wokół szyi i drobne, także perłowe kolczyki. Mówi spokojnie, precyzyjnie dobierając słowa. [...] Równie stonowana, skupiona i uważna jest jej poezja, nasycona szlachetną elegancją, wyrażającą się w równowadze mowy i myśli”. Fragment z rozdziału Poezja uosobiona Anna Legeżyńska snuje wyjątkową opowieść o długim i fascynującym życiu, etosie artystycznym i inteligenckim ważnej poetki, ikonicznej postaci powojennego życia literackiego, a zarazem charyzmatycznej osobowości, jaką była Julia Hartwig. Tę opowieść określa tytułowa wdzięczność - afirmujący stosunek do ludzi, świata i sztuki. Oprócz duchowego portretu Julii Hartwig, wydobywającego to, co nierozpoznane, niedopowiedziane, intymne, Czytelnik otrzymuje również prezentację zakresu tematów, stylów, chwytów lirycznych, którym poetka starała się dochować wierności - wszystkiego tego, co stanowi o oryginalności jej poezji. Życie i twórczość przenikają się w tej książce nieustannie, odsyłając do siebie, a ich wspólną cechą jest sens, który niosą. Seria poświęcona jest wybitnym polskim pisarzom - ich twórczości ujmowanej przez nich samych i interpretowanej przez krytyków jako projekt egzystencjalny, jako próba ustanowienia i zapisania siebie i swojego sposobu odczytywania sensów rzeczywistości, indywidualnego oglądu różnych jej sfer - społecznej, politycznej, etycznej, kulturowej, metafizycznej. Istotnymi kategoriami wyjaśniającymi pisarskie dzieło są w tym przypadku biografia, tożsamość oraz kształtujące je szeroko rozumiane doświadczenie: cielesne i zmysłowe, psychiczne i społeczne, historyczne i polityczne, etniczne i estetyczne, religijne i duchowe... W serii ukazały się tomy: Agnieszka Kałowska, Witkacy. Etyka Marzena Woźniak-Łabieniec, Rymkiewicz. Metafizyka Maciej Urbanowski, Brzozowski. Nowoczesność
Julia High Performance. Design and develop high performing programs with Julia
Avik Sengupta
Julia is a high performance, high-level dynamic language designed to address the requirements of high-level numerical and scientific computing. Julia brings solutions to the complexities faced by developers while developing elegant and high performing code. Julia High Performance will take you on a journey to understand the performance characteristics of your Julia programs, and enables you to utilize the promise of near C levels of performance in Julia.You will learn to analyze and measure the performance of Julia code, understand how to avoid bottlenecks, and design your program for the highest possible performance. In this book, you will also see how Julia uses type information to achieve its performance goals, and how to use multuple dispatch to help the compiler to emit high performance machine code. Numbers and their arrays are obviously the key structures in scientific computing – you will see how Julia’s design makes them fast. The last chapter will give you a taste of Julia’s distributed computing capabilities.
Julia: High Performance Programming. Build powerful and fast systems with Julia
Ivo Balbaert, M E Sherrington, Avik Sengupta
In this learning path, you will learn to use an interesting and dynamic programming language—Julia! You will get a chance to tackle your numerical and data problems with Julia. You’ll begin the journey by setting up a running Julia platform before exploring its various built-in types. We’ll then move on to the various functions and constructs in Julia. We’ll walk through the two important collection types—arrays and matrices in Julia.You will dive into how Julia uses type information to achieve its performance goals, and how to use multiple dispatch to help the compiler emit high performance machine code. You will see how Julia’s design makes code fast, and you’ll see its distributed computing capabilities.By the end of this learning path, you will see how data works using simple statistics and analytics, and you’ll discover its high and dynamic performance—its real strength, which makes it particularly useful in highly intensive computing tasks. This learning path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:? Getting Started with Julia by Ivo Balvaert? Julia High Performance by Avik Sengupta? Mastering Julia by Malcolm Sherrington