摘录Teach Yourself Programming in Ten Years

文章指出在众多领域成为专家约需十年,编程也不例外。给出编程成功的建议,如因兴趣投入、与其他程序员交流、多实践、学习多种编程语言等,还提及不应仅依赖书本学习,同时介绍了寻找优秀软件设计师的计划。

这篇文章教你以什么态度学习编程, 甚合我意, 文章如下

Teach Yourself Programming in Ten Years

Peter Norvig


Researchers (Hayes, Bloom) have shown it takes about ten years to develop expertise in any of a wide variety of areas, including chess playing, music composition, painting, piano playing, swimming, tennis, and research in neuropsychology and topology. There appear to be no real shortcuts: even Mozart, who was a musical prodigy at age 4, took 13 more years before he began to produce world-class music. In another genre, the Beatles seemed to burst onto the scene with a string of #1 hits and an appearance on the Ed Sullivan show in 1964. But they had been playing small clubs in Liverpool and Hamburg since 1957, and while they had mass appeal early on, their first great critical success, Sgt. Peppers, was released in 1967. Samuel Johnson thought it took longer than ten years: "Excellence in any department can be attained only by the labor of a lifetime; it is not to be purchased at a lesser price." And Chaucer complained "the lyf so short, the craft so long to lerne."

Here's my recipe for programming success:

  • Get interested in programming, and do some because it is fun. Make sure that it keeps being enough fun so that you will be willing to put in ten years.

  • Talk to other programmers; read other programs. This is more important than any book or training course.

  • Program. The best kind of learning is learning by doing. To put it more technically, "the maximal level of performance for individuals in a given domain is not attained automatically as a function of extended experience, but the level of performance can be increased even by highly experienced individuals as a result of deliberate efforts to improve." (p. 366) and "the most effective learning requires a well-defined task with an appropriate difficulty level for the particular individual, informative feedback, and opportunities for repetition and corrections of errors." (p. 20-21) The book Cognition in Practice: Mind, Mathematics, and Culture in Everyday Life is an interesting reference for this viewpoint.

  • If you want, put in four years at a college (or more at a graduate school). This will give you access to some jobs that require credentials, and it will give you a deeper understanding of the field, but if you don't enjoy school, you can (with some dedication) get similar experience on the job. In any case, book learning alone won't be enough. "Computer science education cannot make anybody an expert programmer any more than studying brushes and pigment can make somebody an expert painter" says Eric Raymond, author of The New Hacker's Dictionary. One of the best programmers I ever hired had only a High School degree; he's produced a lot of great software, has his own news group, and through stock options is no doubt much richer than I'll ever be.

  • Work on projects with other programmers. Be the best programmer on some projects; be the worst on some others. When you're the best, you get to test your abilities to lead a project, and to inspire others with your vision. When you're the worst, you learn what the masters do, and you learn what they don't like to do (because they make you do it for them).

  • Work on projects after other programmers. Be involved in understanding a program written by someone else. See what it takes to understand and fix it when the original programmers are not around. Think about how to design your programs to make it easier for those who will maintain it after you.

  • Learn at least a half dozen programming languages. Include one language that supports class abstractions (like Java or C++), one that supports functional abstraction (like Lisp or ML), one that supports syntactic abstraction (like Lisp), one that supports declarative specifications (like Prolog or C++ templates), one that supports coroutines (like Icon or Scheme), and one that supports parallelism (like Sisal).

  • Remember that there is a "computer" in "computer science". Know how long it takes your computer to execute an instruction, fetch a word from memory (with and without a cache miss), read consecutive words from disk, and seek to a new location on disk. (Answers here.)

  • Get involved in a language standardization effort. It could be the ANSI C++ committee, or it could be deciding if your local coding style will have 2 or 4 space indentation levels. Either way, you learn about what other people like in a language, how deeply they feel so, and perhaps even a little about why they feel so.

  • Have the good sense to get off the language standardization effort as quickly as possible.

With all that in mind, its questionable how far you can get just by book learning. Before my first child was born, I read all the How To books, and still felt like a clueless novice. 30 Months later, when my second child was due, did I go back to the books for a refresher? No. Instead, I relied on my personal experience, which turned out to be far more useful and reassuring to me than the thousands of pages written by experts.

Fred Brooks, in his essay No Silver Bullets identified a three-part plan for finding great software designers:

  1. Systematically identify top designers as early as possible.

  2. Assign a career mentor to be responsible for the development of the prospect and carefully keep a career file.

  3. Provide opportunities for growing designers to interact and stimulate each other.

This assumes that some people already have the qualities necessary for being a great designer; the job is to properly coax them along. Alan Perlis put it more succinctly: "Everyone can be taught to sculpt: Michelangelo would have had to be taught how not to. So it is with the great programmers".

So go ahead and buy that Java book; you'll probably get some use out of it. But you won't change your life, or your real overall expertise as a programmer in 24 hours, days, or even months.


References

Bloom, Benjamin (ed.) Developing Talent in Young People, Ballantine, 1985.

Brooks, Fred, No Silver Bullets, IEEE Computer, vol. 20, no. 4, 1987, p. 10-19.

Hayes, John R., Complete Problem Solver Lawrence Erlbaum, 1989.

Lave, Jean, Cognition in Practice: Mind, Mathematics, and Culture in Everyday Life, Cambridge University Press, 1988.


Answers

Timing for various operations on a typical 1GHz PC in summer 2001:

execute single instruction 1 nsec = (1/1,000,000,000) sec
fetch word from L1 cache memory 2 nsec
fetch word from main memory 10 nsec
fetch word from consecutive disk location 200 nsec
fetch word from new disk location (seek) 8,000,000nsec = 8msec

Peter Norvig (Copyright 2001)

基于实时迭代的数值鲁棒NMPC双模稳定预测模型(Matlab代码实现)内容概要:本文介绍了基于实时迭代的数值鲁棒非线性模型预测控制(NMPC)双模稳定预测模型的研究与Matlab代码实现,重点在于提升系统在存在不确定性与扰动情况下的控制性能与稳定性。该模型结合实时迭代优化机制,增强了传统NMPC的数值鲁棒性,并通过双模控制策略兼顾动态响应与稳态精度,适用于复杂非线性系统的预测控制问题。文中还列举了多个相关技术方向的应用案例,涵盖电力系统、路径规划、信号处理、机器学习等多个领域,展示了该方法的广泛适用性与工程价值。; 适合人群:具备一定控制理论基础和Matlab编程能力,从事自动化、电气工程、智能制造、机器人控制等领域研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①应用于非线性系统的高性能预测控制设计,如电力系统调度、无人机控制、机器人轨迹跟踪等;②解决存在模型不确定性、外部扰动下的系统稳定控制问题;③通过Matlab仿真验证控制算法的有效性与鲁棒性,支撑科研论文复现与工程原型开发。; 阅读建议:建议读者结合提供的Matlab代码进行实践,重点关注NMPC的实时迭代机制与双模切换逻辑的设计细节,同时参考文中列举的相关研究方向拓展应用场景,强化对数值鲁棒性与系统稳定性之间平衡的理解。
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