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An Essential Reference for Intermediate and Advanced R Programmers Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R. The book develops the necessary skills to produce quality code that can be used in a variety of circumstances. You will learn: The fundamentals of R, including standard data types and functions Functional programming as a useful framework for solving wide classes of problems The positives and negatives of metaprogramming How to write fast, memory-efficient code This book not only helps current R users become R programmers but also shows existing programmers what’s special about R. Intermediate R programmers can dive deeper into R and learn new strategies for solving diverse problems while programmers from other languages can learn the details of R and understand why R works the way it does.
A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.
Søgeord: Aylmer, F.J.; Chamberlain, J. Austen; Suliman Pak; Duff, B.; von der Goltz; Gorringe; Halil Pasha; Lord Hardinge of Penshurst; Kut al Amara; Lake, P.H.N.; General J.E. Nixon; Townshend, C.V.F.; Tyrkiske Hær; Shatt el Arab; Nasiriya; Lord Crewe; Basra; Barrett, A.A.; Baku; Bicharakoff; Dunsterville, L.C.; von Falkenhayn; Marshall, W.R.; Maude, F.S.; Robertson, W.R.; Wilson, H.H.; Baratoff, N.N.; Cobbe, A.S.; Jabal Hamrin; Sannaiyat; Shatt al Adjaim;
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In this essential guide, Meghan Casey outlines a step-by-step approach for successful content strategy, from planning and creating your content to delivering and managing it. Armed with this book, you can confidently tackle difficult activities like explaining clearly to your boss or client what's wrong with their content, getting the budget to do content work, and aligning stakeholders on a common vision. Having The Content Strategy Toolkit at your side is like hiring your own personal consulting firm. You get a complete array of instructions, tools, and templates for most challenges you'll face. In this practical and relevant guide, you'll learn how to: Identify problems with your content and persuade your bosses it's worth the time and resources to do it right Assemble a stellar team for your content project Prepare your organization for content transformation Make sense of your business environment and understand your audience Align stakeholders on business goals and user needs Set a compass for your content and decide how to measure success Create, maintain, and govern on-strategy content You'll learn how to treat content like the strategic asset that it is. "Quality content increases value. Poor-quality content destroys value. It's as simple as that. Meghan's book has specific, practical, and immediately actionable ideas that will help you increase the quality of your content."—Gerry McGovern, CEO, Customer Carewords "This second edition goes deep into three integral topics for content leaders—assembling cross-disciplinary teams, evaluating processes, and building a content playbook. If you're looking to build a new practice or retool an existing one, this book will help you succeed.—Natalie Marie Dunbar, Author, From Solo to Scaled: Building a Sustainable Content Strategy Practice
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. By reading this book, you will learn: The difference between an object and its name, and why the distinction is important The important vector data structures, how they fit together, and how you can pull them apart using subsetting The fine details of functions and environments The condition system, which powers messages, warnings, and errors The powerful functional programming paradigm, which can replace many for loops The three most important OO systems: S3, S4, and R6 The tidy eval toolkit for metaprogramming, which allows you to manipulate code and control evaluation Effective debugging techniques that you can deploy, regardless of how your code is run How to find and remove performance bottlenecks The second edition is a comprehensive update: New foundational chapters: "Names and values," "Control flow," and "Conditions" comprehensive coverage of object oriented programming with chapters on S3, S4, R6, and how to choose between them Much deeper coverage of metaprogramming, including the new tidy evaluation framework use of new package like rlang (http://rlang.r-lib.org), which provides a clean interface to low-level operations, and purr (http://purrr.tidyverse.org/) for functional programming Use of color in code chunks and figures Hadley Wickham is Chief Scientist at RStudio, an Adjunct Professor at Stanford University and the University of Auckland, and a member of the R Foundation. He is the lead developer of the tidyverse, a collection of R packages, including ggplot2 and dplyr, designed to support data science. He is also the author of R for Data Science (with Garrett Grolemund), R Packages, and ggplot2: Elegant Graphics for Data Analysis.