Julia 1.0 By Example
暫譯: Julia 1.0 實例解析

Key Features

• An in-depth exploration of Julia's growing ecosystem of packages by building 4 exciting projects
• Work with the most powerful open-source libraries for machine learning, data wrangling, and data visualization.
• Learn to perform supervised learning, unsupervised learning as well as time series analysis with Julia.

Book Description

Julia is a young programming language that offers a unique combination of performance and productivity that promises to change scientific computing and programming. It also puts performance center stage, achieving C-like execution speed and excellent applications in multi-core, GPU, and cloud computing. After six years in development as an Open Source project, Julia is now ready to take the stage with the release of v1.0. Follow through "Julia v1.0 By Example" for an encompassing exploration of the language by means of progressively engaging examples.

Build practical knowledge and use Julia and its most popular packages to address data science problems and handle generic programming tasks. Beginning with an introduction to the language and its syntax, the book will go on to building the first project where you will learn to analyze and manipulate the Iris dataset using Julia. Then we will explore functions and Julia's type system to build a complex web scraping project. Further, you'll dive into more advanced stuff like supervised machine learning where you'll build a recommender system for a dating website. For the final project, you will go deeper, learning about unsupervised learning, time series, statistics functions as well as visualization with Gadfly and Vega. By the end of the book, you would have gained the practical knowledge, enough to help you build statistical models and projects in Julia.

What you will learn

• Leverage Julia Lang's features, and work with packages.
• Analyze and manipulate dataset using Julia
• Write complex code while building a real-life Julia application
• Utilize functions, user defined types and various control flow available with Julia.
• Develop and execute a web app using Julia and HTTP.Server
• Build a supervised machine learning system with Julia using available packages
• Explore unsupervised machine learning algorithms for data analytics