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Build high-performance applications for scientific computing

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Rust's popularity is growing, due in part to features like memory safety, type safety, and thread safety. But these same elements can also make learning Rust a challenge, even for experienced programmers. This practical guide helps you make the transition to writing idiomatic Rust—while also making full use of Rust's type system, safety guarantees, and burgeoning ecosystem.If you're a software
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Transform your data science workflows with Julia's lightning-fast performance. This practical guide helps Python and R developers transition to Julia for large-scale scientific computing, machine learning, and numerical analysis. Features real-world case studies, optimization techniques, and integration with popular data science tools. Includes parallel computing patterns and best practices for
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Anbefalt for deg

Parallell programmering e-bøker

Parallel R

Concurrent Data Processing in Elixir

High-Performance Programming in C# and .NET

Learning Concurrency in Kotlin

Go Cookbook

Asyncio From Ground Up

Rust Essentials

Machine Learning with Rust, Second Edition

OpenCL Programming Guide

Large-scale Distributed Systems and Energy Efficiency

The OpenMP Common Core

Rust All-in-One For Dummies

Unity 6 ECS Fundamentals

Stream Processing with Apache Spark

Learn R Programming in 24 Hours

C# Multithreaded and Parallel Programming

Programming Phoenix 1.4

Hands-On Design Patterns with Delphi

Learning Julia

Hands-On Concurrency with Rust

Rust Programming By Example

Learning Concurrency in Python

Effective Rust

Julia for Scientific Computing