Publicación sobre Inteligencia Artificial
Advanced R helps you understand how R works at a fundamental level. It is designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages who want to understand what makes R different and special. This book will teach you the foundations of R; three fundamental programming paradigms (functional, object-oriented, and metaprogramming); and powerful techniques for debugging and optimising your code.
If you work in analytics or data science, like we do, you are familiar with the fact that data is being generated all the time at ever faster rates. (You may even be a little weary of people pontificating about this fact.) Analysts are often trained to handle tabular or rectangular data that is mostly numeric, but much of the data proliferating today is unstructured and text-heavy. Many of us who work in analytical fields are not trained in even simple interpretation of natural language.
We developed the tidytext (Silge and Robinson 2016) R package because we were familiar with many methods for data wrangling and visualization, but couldn’t easily apply these same methods to text.
Today, data scientists can choose from an overwhelming collection of exciting technologies and programming languages. Python, R, Hadoop, Julia, Pig, Hive, and Spark are but a few examples. You may already have experience in one or more of these. If so, then why should you still care about the command line for doing data science? What does the command line have to offer that these other technologies and programming languages do not?
El diseño del kit de herramientas permite trabajar en paralelo entre más de 70 idiomas, utilizando el formalismo de Dependencias Universales. Stanza está construido con componentes de red neuronal de alta precisión, que también permiten una capacitación y evaluación eficientes con sus propios datos anotados.
Packages are the fundamental units of reproducible R code. They include reusable R functions, the documentation that describes how to use them, and sample data. In this book you’ll learn how to turn your code into packages that others can easily download and use. Writing a package can seem overwhelming at first. So start with the basics and improve it over time. It doesn’t matter if your first version isn’t perfect as long as the next version is better. This is where we are developing the 2nd edition of this book.
In Implementing a Custom Language Succinctly, Succinctly series author Vassili Kaplan demonstrated how to create a customized programming language. Now, he returns to showcase how you can use that language to build fully functional mobile apps. In Writing Native Mobile Apps in a Functional Language Succinctly, you will build off the skills you’ve already developed to begin creating applications that you can put to immediate use.
The R programming language on its own is a powerful tool that can perform thousands of statistical tasks, but by writing programs in R, you gain tremendous power and flexibility to extend its base functionality. Senior Succinctly series author and editor James McCaffrey shows you how in R Programming Succinctly.
In a company, when the business process is defined, employees feel confident in their roles. They understand expectations and can easily identify when something is amiss or requires escalation. In Business Process Flow Mapping Succinctly, Erica Quigley will show you the activities that should occur in order to completely define a business process so you can identify and rectify gaps in communication, procedures, and teams. You will also learn a simple way to document the process so the project team working with you can feel confident in what they are being asked to execute.
