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It is not so often in life that you get a second chance. I remember that only days after we stopped editing the first edition, I kept asking myself, "Why didn't I...?", or "What the heck was I thinking saying it like that?", and on and on. In fact, the first project I started working on after it was published had nothing to do with any of the methods in the first edition. I made a mental note that if given the chance, it would go into a second edition. When I started with the first edition, my goal was to create something different, maybe even create a work that was a pleasure to read, given the constraints of the topic. After all the feedback I received, I think I hit the mark. However, there is always room for improvement, and if you try and be everything to all people, you become nothing to everybody. I'm reminded of one of my favorite Frederick the great quotes, "He who defends everything, defends nothing". So, I've tried to provide enough of the skills and tools, but not all of them, to get a reader up and running with R and machine learning as quickly and painlessly as possible. I think I've added some interesting new techniques that build on what was in the first edition. There will probably always be the detractors who complain it does not offer enough math or does not do this, that, or the other thing, but my answer to that is they already exist! Why duplicate what was already done, and very well, for that matter? Again, I have sought to provide something different, something that would keep the reader's attention and allow them to succeed in this competitive field. Before I provide a list of the changes/improvements incorporated into the second edition, chapter by chapter, let me explain some universal changes. First of all, I have surrendered in my effort to fight the usage of the assignment operator <- versus just using =. As I shared more and more code with others, I realized I was out on my own using = and not <-. The first thing I did when under con

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《C++ Template》第二版，2017年9月16日出版 Templates are among the most powerful features of C++, but they remain misunderstood and underutilized, even as the C++ language and development community have advanced. In C++ Templates, Second Editi on, three pioneering C++ experts show why, when, and how to use modern templates to build software that’s cleaner, faster, more efficient, and easier to maintain. Now extensively updated for the C++11, C++14, and C++17 standards, this new edition presents state-of-the-art techniques for a wider spectrum of applications. The authors provide authoritative explanations of all new language features that either improve templates or interact with them, including variadic templates, generic lambdas, class template argument deduction, compile-time if, forwarding references, and user-defined literals. They also deeply delve into fundamental language concepts (like value categories) and fully cover all standard type traits. The book starts with an insightful tutorial on basic concepts and relevant language features. The remainder of the book serves as a comprehensive reference, focusing first on language details and then on coding techniques, advanced applications, and sophisticated idioms. Throughout, examples clearly illustrate abstract concepts and demonstrate best practices for exploiting all that C++ templates can do. Understand exactly how templates behave, and avoid common pitfalls Use templates to write more efficient, flexible, and maintainable software Master today’s most effective idioms and techniques Reuse source code without compromising performance or safety Benefit from utilities for generic programming in the C++ Standard Library Preview the upcoming concepts feature The companion website, tmplbook.com, contains sample code and additional updates.