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The Julia Language Handbook

Author: George Root

Publisher:

Published: 2019-02-02

Total Pages: 199

ISBN-13: 9781794681699

If you are new to Julia and want a reference that describes how to install and use Julia, this is the book you want. Many of the other Julia books available describe previous versions with examples that no longer work. The "Julia Handbook" is current as of Julia v1.02 and every example, of which there are dozens, has been tested and they all work.You will learn how to install and use the Julia REPL mode and the Jupyter Notebook mode to create and test your code. Other topics include:Data TypesFunctions and PackagesTuplesData ArraysData FramesData StructuresFlow ControlLoops and IterationInput / Output - formatted printing - writing and reading data filesLine and Scatter PlotsOther Plot TypesRandom NumbersOptimization Using Optim and JuMPThis is the book I wanted to buy when I started learning Julia but I had to write it myself to get all of the detail and up-to-date information I wanted. If you are just learning Julia you will find this to be a useful guide. If you are already using Julia you will find this to be an excellent reference book to remind you of some obscure Julia syntax.

The Cambridge Handbook of Endangered Languages

Author: Peter K. Austin

Publisher: Cambridge University Press

Published: 2011-03-24

Total Pages: 581

ISBN-13: 113950083X

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It is generally agreed that about 7,000 languages are spoken across the world today and at least half may no longer be spoken by the end of this century. This state-of-the-art Handbook examines the reasons behind this dramatic loss of linguistic diversity, why it matters, and what can be done to document and support endangered languages. The volume is relevant not only to researchers in language endangerment, language shift and language death, but to anyone interested in the languages and cultures of the world. It is accessible both to specialists and non-specialists: researchers will find cutting-edge contributions from acknowledged experts in their fields, while students, activists and other interested readers will find a wealth of readable yet thorough and up-to-date information.

Data Science with Julia

Author: Paul D. McNicholas

Publisher: CRC Press

Published: 2019-01-02

Total Pages: 186

ISBN-13: 1351013653

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"This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist."- Professor Charles Bouveyron, INRIA Chair in Data Science, Université Côte d’Azur, Nice, France Julia, an open-source programming language, was created to be as easy to use as languages such as R and Python while also as fast as C and Fortran. An accessible, intuitive, and highly efficient base language with speed that exceeds R and Python, makes Julia a formidable language for data science. Using well known data science methods that will motivate the reader, Data Science with Julia will get readers up to speed on key features of the Julia language and illustrate its facilities for data science and machine learning work. Features: Covers the core components of Julia as well as packages relevant to the input, manipulation and representation of data. Discusses several important topics in data science including supervised and unsupervised learning. Reviews data visualization using the Gadfly package, which was designed to emulate the very popular ggplot2 package in R. Readers will learn how to make many common plots and how to visualize model results. Presents how to optimize Julia code for performance. Will be an ideal source for people who already know R and want to learn how to use Julia (though no previous knowledge of R or any other programming language is required). The advantages of Julia for data science cannot be understated. Besides speed and ease of use, there are already over 1,900 packages available and Julia can interface (either directly or through packages) with libraries written in R, Python, Matlab, C, C++ or Fortran. The book is for senior undergraduates, beginning graduate students, or practicing data scientists who want to learn how to use Julia for data science. "This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist." Professor Charles Bouveyron INRIA Chair in Data Science Université Côte d’Azur, Nice, France

Getting Started with Julia

Author: Ivo Balbaert

Publisher: Packt Publishing Ltd

Published: 2015-02-26

Total Pages: 214

ISBN-13: 1783284803

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This book is for you if you are a data scientist or working on any technical or scientific computation projects. The book assumes you have a basic working knowledge of high-level dynamic languages such as MATLAB, R, Python, or Ruby.

The Career Coaching Handbook

Author: Julia Yates

Publisher: Routledge

Published: 2013-11-20

Total Pages: 249

ISBN-13: 1317963482

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Uniquely combining the latest research into careers with the most up to date coaching approaches, Julia Yates shows how to effectively apply coaching techniques to the world of career support. Demonstrating how coaching research explains practice and how practice benefits from research, The Career Coaching Handbook is accessibly written with a solid evidence-based foundation. Presented in three parts, the book covers developments in theory and research and applies this knowledge to the real world. Part 1, Theories of Career, looks at 21st century career paths, job satisfaction and career changes – both planned and unplanned. Part 2, Career Coaching Approaches, looks at coaching strategies that are applicable to career coaching in particular. Part 3, Coaching into the World of Work, covers specific real-world situations where coaching is beneficial, from job search strategies to CV and interview coaching. Evidence and research is used throughout to demonstrate the most effective strategies for coaching. The Career Coaching Handbook provides an essential introduction for students or practitioners who are interested in developing their own practice, finding new and improved ways to do things and understanding the theories that underpin effective career coaching practice.

Julia 1.0 Programming Complete Reference Guide

Author: Ivo Balbaert

Publisher: Packt Publishing Ltd

Published: 2019-05-20

Total Pages: 455

ISBN-13: 1838824677

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Learn dynamic programming with Julia to build apps for data analysis, visualization, machine learning, and the web Key FeaturesLeverage Julia's high speed and efficiency to build fast, efficient applicationsPerform supervised and unsupervised machine learning and time series analysisTackle problems concurrently and in a distributed environmentBook 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 BalbaertJulia Programming Projects by Adrian SalceanuWhat you will learnCreate your own types to extend the built-in type systemVisualize your data in Julia with plotting packagesExplore the use of built-in macros for testing and debuggingIntegrate Julia with other languages such as C, Python, and MATLABAnalyze and manipulate datasets using Julia and DataFramesDevelop and run a web app using Julia and the HTTP packageBuild a recommendation system using supervised machine learningWho 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 mathematics and programming is a must.

The Cambridge Handbook of Second Language Acquisition

Author: Julia Herschensohn

Publisher: Cambridge University Press

Published: 2018-09-06

Total Pages: 0

ISBN-13: 9781108733748

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What is language and how can we investigate its acquisition by children or adults? What perspectives exist from which to view acquisition? What internal constraints and external factors shape acquisition? What are the properties of interlanguage systems? This comprehensive 31-chapter handbook is an authoritative survey of second language acquisition (SLA). Its multi-perspective synopsis on recent developments in SLA research provides significant contributions by established experts and widely recognized younger talent. It covers cutting edge and emerging areas of enquiry not treated elsewhere in a single handbook, including third language acquisition, electronic communication, incomplete first language acquisition, alphabetic literacy and SLA, affect and the brain, discourse and identity. Written to be accessible to newcomers as well as experienced scholars of SLA, the Handbook is organised into six thematic sections, each with an editor-written introduction.

Handbook of Language and Literacy, Second Edition

Author: C. Addison Stone

Publisher: Guilford Publications

Published: 2016-05-27

Total Pages: 736

ISBN-13: 1462527485

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An acclaimed reference that fills a significant gap in the literature, this volume examines the linkages between spoken and written language development, both typical and atypical. Leading authorities address the impact of specific language-related processes on K-12 literacy learning, with attention to cognitive, neurobiological, sociocultural, and instructional issues. Approaches to achieving optimal learning outcomes with diverse students are reviewed. The volume presents research-based practices for assessing student needs and providing effective instruction in all aspects of literacy: word recognition, reading comprehension, writing, and spelling. New to This Edition *Chapters on digital literacy, disciplinary literacy, and integrative research designs. *Chapters on bilingualism, response to intervention, and English language learners. *Incorporates nearly a decade's worth of empirical and theoretical advances. *Numerous prior edition chapters have been completely rewritten.

Julia for Data Science

Author: Zacharias Voulgaris

Publisher:

Published: 2016

Total Pages: 0

ISBN-13: 9781634621304

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After covering the importance of Julia to the data science community and several essential data science principles, we start with the basics including how to install Julia and its powerful libraries. Many examples are provided as we illustrate how to leverage each Julia command, dataset, and function. Specialized script packages are introduced and described. Hands-on problems representative of those commonly encountered throughout the data science pipeline are provided, and we guide you in the use of Julia in solving them using published datasets. Many of these scenarios make use of existing packages and built-in functions, as we cover: An overview of the data science pipeline along with an example illustrating the key points, implemented in Julia Options for Julia IDEs Programming structures and functions Engineering tasks, such as importing, cleaning, formatting and storing data, as well as performing data preprocessing Data visualization and some simple yet powerful statistics for data exploration purposes Dimensionality reduction and feature evaluation Machine learning methods, ranging from unsupervised (different types of clustering) to supervised ones (decision trees, random forests, basic neural networks, regression trees, and Extreme Learning Machines) Graph analysis including pinpointing the connections among the various entities and how they can be mined for useful insights. Each chapter concludes with a series of questions and exercises to reinforce what you learned. The last chapter of the book will guide you in creating a data science application from scratch using Julia.

Don't Get Me Wrong!

Author: Julia Grosse

Publisher: Bierke Publishing (Acc)

Published: 2010

Total Pages: 0

ISBN-13: 9783981337099

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Photographs of hands gestures show the differences between cultures around the world.