This book, the first available on Crystal, shows you how to write applications that have the beauty and elegance of a modern language, combined with the power of types and modern concurrency tooling. Now you can write beautiful code that runs faster, scales better, and is a breeze to deploy.
Julia is a new open-source programming language that is used in the field of data science computing. It was created to solve the dilemma between high-level slow code and fast (but low-level) code, and the necessity to use both to achieve high performance. This book covers all you need to know about Julia to leverage its high speed and efficiency.
Starting by comparing Rust with other programming languages, this book will show you where and how to use Rust. It will discuss primitive types along with variables and their scope, binding and casting, simple functions, and ways to control execution flow in a program.Next, the book covers flexible arrays, vectors, tuples, enums, and structs. You will then generalize the code with higher-order functions and generics applying it to closures, iterators, consumers, and so on. Memory safety is ensured by the compiler by using references, pointers, boxes, reference counting, and atomic reference counting. You will learn how to build macros and crates and discover concurrency for multicore execution.By the end of this book, you will have successfully migrated to using Rust and will be able to use it as your main programming language.
Learn 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..
A key problem of software development today is the software bloat, where huge toolchains and development environments are needed in software coding and deployment. Red significantly reduces this bloat by offering a minimal but complete toolchain. This is the first introductory book on Red, and it will get you up and running as quickly as possible.
The book offers an in-depth exploration of the open source programming language Go, focusing on its first stable release, Go 1, developed by Google. It covers the language's features, syntax, and practical applications, making it an essential resource for programmers looking to understand and utilize Go effectively.