藤泽秀行

● 作为有修养的专业棋手，赢了棋也不会特别高兴，而且，在败者面前喧嚷是有失棋品的。我认为，胜负就只是个结果，在大胜负中赢了也不必有登了天的气氛，胜负的余韵一旦消失，又回到原本的自己。对对手，我也是如此说。胜负单纯只是个结果，又不是真正的战争被杀了，所以，我对败者不使用多余的神经。用这种心情站起来，把这次胜负画上句号，而且，对手越强，我产生再一次和这人下的心情也就越强、越多。
 

○ 我总觉得要在有生之年，尽力追求自己的最善。

 

● 我过于相信别人吃过许多亏，但即使这样，我也从不去怀疑朋友。

 

○ 生死不由己，为了下出自己人生最善的一手去继续努力。

 

● 不要过于捞空贪利，而是要坚实地积蓄力量，为将来奠定基础，战斗起来也就有力。所谓“厚”的棋，含义就在于此。当抓住对方的弱处后，狠狠一击，空自然就来了。人生也是如此，恍然一见进程很慢，却是在不断地蓄存力量，一有时机就爆发出来，一口气追上去便遥遥领先。

 

○ 不过，不管蓄存了多少力量，抓不着机会就没有价值。而且，即使抓住了机会，不擅长使用也达不到预期的结果。在棋上光靠“厚”是绝对赢不了棋的。应用有误，犹如把宝物变成了废物。

 

● 能努力到什么程度，也是人才能的一部分。

 

○ 在年轻时，不要去为自己的力量不足而烦恼，只要全力以赴去拼就行了，反正不知道自己的潜力，也没必要去深深地苦恼。

 

● 体育界，新人一破世界纪录，就会引起大骚动，这中间有本人的天才素质，但我却认为是在那背后努力的结果。他本人在努力的基础上持有了自信，破纪录只是顺理成章的事，就是这么回事。

 

○ 什么事都不要想得太复杂，虽然说法比较古老，不管在什么时候，神都会保佑好人的。这么去想就行了。

 

● 当感到这就是自己最大的限度时，就开始衰老了。

 

○ 左右人生的是余白的部分。在接受加藤正夫君挑战的第二次“棋圣战”的第五局，我用了２小时５７分的大长考，将对方的大龙吃了，这个大长考把大部分变化都算透了，但还是存在没有算到的地方。在接近无限的变化中，我只是把有限的变化算到了，也就是说，这手棋在这里是不是唯一的、最善的一手，我没有自信。同时，在我没有算到的部分里藏有的最善手，被加藤君发现的可能性也是存在的。如果，在最後我输了，只能说我还不成熟，这是没有办法的事。计算虽然复杂，也只有勇敢地去下了。结果，是我计算正确，赢了。于是，我总觉得是一种“运”的存在。

 

● 我从来认为围棋是搞不明白的，只是尽力去想搞明白。所以，在我不明白的地方，也就是没算到的余白部分，那就是“运”的因素在起作用了。以我的经验，“运气”是不可思议的来回循环的东西。所以，当认为运气不好时，忍耐等待是最好的办法，只要想，苍天是维护正道的，也就能忍耐了。当然，为了得到苍天的维护正道，不进行应有的努力是不行的。

 

○ 书法，可以表现出人生。在我的书法里，我感到有种“气”的东西。我决不认为我的字高明，但我写的字就是我的人生，从行笔之中，能看出从地狱里生返的男人的人生哲学。

 

● 把问题想得越复杂，问题也就越难了。把什么都权当不懂去学习，人也就变得充实不管是棋，还是人生，都充满了意外性。这世上，若能按计划进行的话，也就不用去辛苦了。

 

○ 当人能吃饱饭时，就开始变弱。

 

● 如果自己的所思、所想，没有表现的场所，那人生一定会变得很孤寂很无聊。

 

○  棋也好，人生也好，要和强者交往。

 

● 若怕酒醉人，最初就不要沾口；若惧赌危险，开始就不要接近。酒既可是良药，也可成毒药。

 

○ 人生，要活得潇洒，豪爽和侠义。男人的价值不是金钱、名誉，比起女人来更由“人生哲学”来决定。

2013 Fall 大致计划与想法 （附华老诗）

这学期最重要的原则即是——认准目标，然后一股脑投入进去。如果不能练就一种做事的气质，那就不可能成为一个合格的phd。

最重要的目标无疑是computing的加强，除了多写程序，多调试，没有它途。当然，现下有益的做法是每天都花足够的时间静心读书，寻找读书的感觉，我之前在这点上放纵太久了，不改变不行。

先理理想养成的习惯：

1. 每天都坚持做足够多的独立思考，并表达自我（方式比如说出来、写下来）。每天给自己15分钟无拘无束的思考时间。

2. 挤时间，挤时间，再挤时间。对现在的我来说，要多学东西，非如此不可。

3. 做事理好顺序，特别是判断清什么事重要但不急迫，什么事急迫但不重要。

4. 体技心一体，如果效率低下，则应该多加强运动。晨跑是个好东西。

5. 保持一个好的心态。心态需要调和，有时热烈，有时平静。

6. 找回些视觉记忆的感觉来！睡前多在脑海里放电影，记录重要的东西~

说到底，形成一种好的气质，并强烈地贯彻它！

 

学期读书书单一：

1. Art of R programming

2. Selected Chapters in Hastie’s ESL

3. Nonlinear Programming  选章

4. Stat and Truth by Rao

 

九月大致计划：

1.为R编程打下好的基础，研读Art一书并做多R的编程实践。

2. 没事多鼓捣鼓捣R和Latex，找感觉~

3. 多泡COS，数据科学、R客等编程网站或博客

4. Follow老板data mining的课，并完成作业

5. 为现project所需的数学做回顾和积累

 

更远一点，或者说闲暇时候可做的事：

1.Excel、PPT之类的基本技能

2.简历的准备

3.了解实习的申请

 

最后，用华老诗自勉，新学期努力加餐饭~

神奇妙算古名词，师承前人沿用之。神奇化易是坦道，易化神奇不足提。

妙算还从拙中来，愚公智叟两分开。积久方显愚公智，发白才知智叟呆。

埋头苦干是第一，熟练生出百巧来。勤能补拙是良训，一分辛苦一分才。

                                                  —— 华罗庚 《从孙子的【神奇妙算】谈起》

 

CS学习资源

课程来源： Udacity, Cousera,网易公开课

推荐课程：

0, MIT计算机科学导论，5星。请到网易公开课找，或者iTunes U等找英文资源。我上课的时间是大四。讲的内容基本是以python编程为主，并且会涉及到一定的OOC（面向对象）的内容，鉴于后面的课都跟OOC没什么关系所以这个课也还是挺好的。

2, Udacity CS262 Programming Language：5星，通过build一个javascript和html的interpreter可以对计算机语言的运行方式有一个更深层次的理解。尤其是对于各种syntax error之类的。而且他的成品基本上是Udacity所有课里面最exciting的，老师的声音也很好听。难度适中。有前两个的基础应该问题不大

4, Coursera Machine Learning：4星，ML必须课需要说什么么。。。不过比较偏应用，会介绍Neural Network，但是对SVM基本上一带而过。还有recommendation system和别的一些较应用的内容。没有reinforcement learning的部分，unsupervised也比较浅。有PA，没有期末考试，一般人这课都能拿满分吧因为没有限制尝试的次数。。。用的语言是Octave/Matlab，难度一般。

15 Stanford Machine Learning: 4星。是iTunes U上面的，Andrew Ng在斯坦福的讲课视频，相比前面coursera的就更理论，虽然没有NN的内容，但是svm讲得很细，还有ica和reinforcement的部分。总之算是巩固基础，然后相辅相成。同样我还是很喜欢吴恩达老师的口音！

7, Udacity CS253 Web Application: 3星，挺不错的课，就是最后用GAP搭建一个非常简单的blog以及wiki。能够提供一些关于网页应用的insight(当然非常浅)，做的东西也算是非常有意思的，另外用的平台是Google的GAP，国内的同学请准备翻墙。难度适中。而且最后一单元会谈到很多很实用的问题比如scale什么的。而且能给一些关于software engineering的idea。

8, Coursera Algorithms: Design and Analysis Part 1： 5星，这个是Stanford开的那版，不是Princeton的，后者我没上过不过据说更浅一些。老师很有激情语速也比较快，写字也很难看。。。不过看多了就习惯了。算法对CS是非常重要的，也是面试常考的。这个介绍的是基本概念big-O，还有sort和search。每周都有PA，基本是给input然后求output这样，不限定语言，不过python有时候会非！常！慢！难度适中

17, Udacity CS222 Differential Equation：3星，在学校基本算是没学过微分方程所以挺遗憾的。。。这个课也有涉及很多实际问题所以算是有趣。画的图也很好看。。。总之最后的感觉就是世界真和谐，世界真奇妙，世界真美好。而且用matplotlib，需要的同学可以借鉴一下。

9, Coursera Probabilistic Graphical Models: 3.5星，和Machine Learning的关系也没有那么大，还不算一定必选。老师是Coursera的另一位cofounder，内容是研究生级别的，很难，PA也很难。我现在有些概念也没完全理解透。。。而且内容很多。借用weibo上老师木的评价：“别的都是讲的术，图模型讲的是道”。自虐指数三星。我当时经常周六下午做这个PA做的死去活来。。。

20, Coursera Neural Networks for Machine Learning： 4星。现在Deep Learning的领军人大牛hinton亲自讲授。内容有点。。。晦涩，但是理解之后概念还是不错的。PA什么的难度也适中。不算特别变态。

23, Udacity CS271 Intro to Artificial Intelligence: 4星。Udacity当年的第一门课。两个cofounder讲。对于ML，NLP，CV，机器人，game theory等都有所涉及。看完了我突然觉得。。。尼玛原来我感兴趣的这些全都是AI啊。。。不难，没有PA，花点时间就好了。

24, Coursera Algorithms: Design and Analysis Part 2: 5星。必须的五星，之前的part 2，内容是greedy algorithm，dynamic programming和NP。涉及的东西很多，PA也变态了很多python真的特别慢。在此力荐pypy。没什么可说的算法是必须看的。而且这俩part加起来本科毕业生的水平至少就有了。。。

26, Coursera: Intro to Database: 3星 现在搬到Class2Go上面去了貌似。介绍数据库，包括一些xml啊json什么的还有nosql的部分。当然大头是SQL，因为考SAS证的时候学过了，所以也就看看。不过数据库对于big data什么的还是很重要的（准确地说nosql数据库还有DFS什么的很重要。。。），所以应该还是看看比较好。

37, Intro to Data Science: 4星，最近发现UW的课出其不意的很靠谱啊。。。这个课只上了第一周但是感觉来说是非常靠谱的，实用性很强，虽然相比CS感觉会更适合统计的同学。。。不过目前的感觉就是如果有志于做Data Scientist请一定要上这门课。会使用Python，SQL，R，基本上这些Data Scientist也是必须的。

总结：必上：MIT的导论，Udacity 262, 212，Coursera上斯坦福的算法。还有Andrew在Coursera和Stanford上面的两版Machine Learning。

转入长期，闭关修行

1. 抓住这个暑假的时光养成一些好习惯。读书思考、自我检验和总结、运动、休息，这是关乎“体、技、心”三位一体的修炼。

2.戒微博一段时间

3.多和可以讨论和学习的人交流

PS：

姑娘回复的备份：

“早上起来上班看到有这么多留言（川注：我吐槽她对长辈说的理由的。。）。。好吧，【太熟悉】确实是在背逼着表态时敷衍大人的理由。。我真实的想法上次你回来本就想对你说，然后被你打断了=_=。。 我现在就是不想谈恋爱，更不想结婚，觉得现在的状态挺好。（这原因一丢给我妈她肯定会说什么女孩子不必男孩子经不起熬啊过了多少岁就难找着好的了什么什么的一大堆。。）我还以为上次你打断是get到了。。=_=。。 我并没有刻意要考验你，也做不来别人当备胎存着或是骑驴找马的技术活，只是觉得以来这远距离的慢慢你也就淡了，而来拖延症的人-_-#。。
恩以上就是我的想法。。 一早起来头晕得很，表述什么的也没法斟酌，你那啥就自行转换吧嘿嘿。。 ”

车到山前必有路，不必太拘泥于局部（比如小利小害），所谓“疾风迅雷”

如题

看来我之前想多了。。

晚上无意间到冬冬校内回顾了这些年她留言板里的留言，还看到了好几条自己当年的留的。。 知道了她腰这毛病大约本科时候就有了，哎，当时我也忒没注意了。另，还看到了她和很多她的同学朋友的对话，真的是非常可爱和宝贝的女娃子，哈哈。不过看到八皮和她的亲昵言语，我还是有点小嫉妒的，不过身为男儿，自当有男儿的表达方式。

恩，她很美好，我愿为她纯粹持续地燃烧！

何谓象

弘一法师圆寂时留有佛偈，“君子之交，其淡如水；执象而求，咫尺千里。问余何适，廓而忘言；华枝春满，天心月圆。”

“执象而求，咫尺千里”，说的真好。而何谓”象“呢，在我看来，这里的”执象”乃是说抱着功利的心态或者设定了错误的目标方向，一旦这样去求，即使是在你认为近在咫尺的时候，你也仍距事情的真相、事物的本源有千里之遥。

我喜欢冬冬，可我不愿执象而求。我不会迷恋她，在心底我单纯地觉得她温暖可爱，并想刚柔并济地去爱她。

喜欢一个人如是，做一件事也一样，若能看清何谓象，才能够明心，进而才能有力地做取舍。

 

What I wanna achieve in the process of preparing oral(52 days left)

To summarize, that is:

1.To get a universal view of Inference and Regression and if time allow, also try to get a universal view of the mathematics that I’ve learned.
2.To gain deeper understanding at some particular point, if do not have enough time, at least get some idea what view that way would lead to(Usually, here you might also need to stand at some other places to look at it). It’s not possible to fulfill this without seeing some egs and work out some problems by myself.
3.Form good habits of thinking and learning. For instance, the appetite of solving problems and the motivation to investigate.
4.Avoid all unnecessary events, submerge in your own world and live with strength!
5.Now I have a feeling that if you can not maintain consistency and intensity at the same time, then consistency is preferable to intensity under most occasions. So, be consistent, either in study and in love, i.e. if you determine to learn from one book, then you should follow that book(or at least that related part in that book) thoroughly and resist the temptation to pick up another one in the middle.

PS:
The math that I felt more and more important to my current research field:
1. Analysis, i.e. Integration techniques, Taylor expansion(in high dim, Hessian matrix~), Conditional Optimality, some series and function convergence examples, Modes of convergence

And I found the need to learn complex analysis, like its integration theory and analytic theory(one eg is the integration of (sinx/x)^2). The other important discipline is Fourier analysis, I need to get a sufficient understanding in this field. Like the space structure, Fourier transform, FFT, the idea of Kernel and smoothing etc. If I do this, I should be able to tell why Fourier analysis plays an important role in probability, or in particular, time series study.

2. Algebra, i.e. Linear transf and its geometric meaning(like real symm, Householder transf, Givens. Here you definitely need to understand some spectral theory of matrix), unitary space and the idea of projection and orthogonality(note the conditional expectation!), matrix decomposition(QR, SVD, Triangular, Polar, etc), matrix diagonalization(also consider the partition matrices), matrix canonical forms, Quadratic forms and its relationship with ellipse and other geometry objects( This also enable me to see the corresponding application in stat), etc.

For abstract algebra, its notion and idea also plays important role in some branches of stat, say, invariant theory and of course, the matrix and space theory behind regression and time series.

And some stat methods and algorithms I need to know:
1. EM
2. Gradient descent( and the stochastic version)
3. PCA, LDA
4. Idea of classification
5. GLM
6. LASSO, LARS
7. Comparison about Frequentist and Bayesian approach in stat(so, read that Springer book!)

Teach Yourself Programming in Ten Years

用十年来学编程
Peter Norvig
为什么每个人都急不可耐？

走进任何一家书店，你会看见《Teach Yourself Java in 7 Days》（7天Java无师自通）的旁边是一长排看不到尽头的类似书籍，它们要教会你Visual Basic、Windows、Internet等等，而只需要几天甚至几小时。我在Amazon.com上进行了如下搜索：
pubdate: after 1992 and title: days and (title: learn or title: teach yourself)
(出版日期：1992年后 and 书名：天 and （书名：学会 or 书名：无师自通）)
我一共得到了248个搜索结果。前面的78个是计算机书籍（第79个是《Learn Bengali in 30 days》，30天学会孟加拉语）。我把关键词“days”换成“hours”，得到了非常相似的结果：这次有253本书，头77本是计算机书籍，第78本是《Teach Yourself Grammar and Style in 24 Hours》（24小时学会文法和文体）。头200本书中，有96%是计算机书籍。
结论是，要么是人们非常急于学会计算机，要么就是不知道为什么计算机惊人地简单，比任何东西都容易学会。没有一本书是要在几天里教会人们欣赏贝多芬或者量子物理学，甚至怎样给狗打扮。在《How to Design Programs》这本书里说“Bad programming is easy. Idiots can learn it in 21 days, even if they are dummies.” （坏的程序是很容易的，就算他们是笨蛋白痴都可以在21天内学会。）
让我们来分析一下像《Learn C++ in Three Days》（3天学会C++）这样的题目到底是什么意思：
学会：在3天时间里，你不够时间写一些有意义的程序，并从它们的失败与成功中学习。你不够时间跟一些有经验的程序员一起工作，你不会知道在C++那样的环境中是什么滋味。简而言之，没有足够的时间让你学到很多东西。所以这些书谈论的只是表面上的精通，而非深入的理解。如Alexander Pope（英国诗人、作家，1688-1744）所言，一知半解是危险的（a little learning is a dangerous thing）
C++：在3天时间里你可以学会C++的语法（如果你已经会一门类似的语言），但你无法学到多少如何运用这些语法。简而言之，如果你是，比如说一个Basic程序员，你可以学会用C++语法写出Basic风格的程序，但你学不到C++真正的优点（和缺点）。那关键在哪里？Alan Perlis（ACM第一任主席，图灵奖得主，1922-1990）曾经说过：“如果一门语言不能影响你对编程的想法，那它就不值得去学”。另一种观点是，有时候你不得不学一点C++（更可能是javascript和Flash Flex之类）的皮毛，因为你需要接触现有的工具，用来完成特定的任务。但此时你不是在学习如何编程，你是在学习如何完成任务。
3天：不幸的是，这是不够的，正如下一节所言。
10年学编程

一些研究者（Bloom (1985), Bryan & Harter (1899), Hayes (1989), Simmon & Chase (1973)）的研究表明，在许多领域，都需要大约10 年时间才能培养出专业技能，包括国际象棋、作曲、绘画、钢琴、游泳、网球，以及神经心理学和拓扑学的研究。似乎并不存在真正的捷径：即使是莫扎特，他4 岁就显露出音乐天才，在他写出世界级的音乐之前仍然用了超过13年时间。再看另一种音乐类型的披头士，他们似乎是在1964年的Ed Sullivan节目中突然冒头的。但其实他们从1957年就开始表演了，即使他们很早就显示出了巨大的吸引力，他们第一次真正的成功——Sgt. Peppers——也要到1967年才发行。Malcolm Gladwell 研究报告称，把在伯林音乐学院学生一个班的学生按水平分成高中低，然后问他们对音乐练习花了多少工夫：
在这三个小组中的每一个人基本上都是从相同的时间开始练习的（在五岁的时候）。在开始的几年里，每个人都是每周练习2-3个小时。但是在八岁的时候，练习的强度开始显现差异。在这个班中水平最牛的人开始比别人练习得更多——在九岁的时候每周练习6个小时，十二岁的时候，每周8个小时，十四岁的时候每周16个小时，并在成长过程中练习得越来越多，到20岁的时候，其每周练习可超过30个小时。到了20岁，这些优秀者在其生命中练习音乐总共超过 10,000 小时。与之对比，其它人只平均有8,000小时，而未来只能留校当老师的人仅仅是4,000 小时。

所以，这也许需要10,000 小时，并不是十年，但这是一个magic number。Samuel Johnson（英国诗人）认为10 年还是不够的：“任何领域的卓越成就都只能通过一生的努力来获得；稍低一点的代价也换不来。”（Excellence in any department can be attained only by the labor of a lifetime; it is not to be purchased at a lesser price.） 乔叟（Chaucer，英国诗人，1340-1400）也抱怨说：“生命如此短暂，掌握技艺却要如此长久。”（the lyf so short, the craft so long to lerne.）
下面是我在编程这个行当里获得成功的处方：
对编程感兴趣，因为乐趣而去编程。确定始终都能保持足够的乐趣，以致你能够将10年时间投入其中。
跟其他程序员交谈；阅读其他程序。这比任何书籍或训练课程都更重要。
编程。最好的学习是从实践中学习。用更加技术性的语言来讲，“个体在特定领域最高水平的表现不是作为长期的经验的结果而自动获得的，但即使是非常富有经验的个体也可以通过刻意的努力而提高其表现水平。”（p. 366），而且“最有效的学习要求为特定个体制定适当难度的任务，有意义的反馈，以及重复及改正错误的机会。”（p. 20-21）《Cognition in Practice: Mind, Mathematics, and Culture in Everyday Life》（在实践中认知：心智、数学和日常生活的文化）是关于这个观点的一本有趣的参考书。
如果你愿意，在大学里花上4年时间（或者再花几年读研究生）。这能让你获得一些工作的入门资格，还能让你对此领域有更深入的理解，但如果你不喜欢进学校，（作出一点牺牲）你在工作中也同样能获得类似的经验。在任何情况下，单从书本上学习都是不够的。“计算机科学的教育不会让任何人成为内行的程序员，正如研究画笔和颜料不会让任何人成为内行的画家”, Eric Raymond，《The New Hacker’s Dictionary》（新黑客字典）的作者如是说。我曾经雇用过的最优秀的程序员之一仅有高中学历；但他创造出了许多伟大的软件（XEmacs, Mozilla），甚至有讨论他本人的新闻组，而且股票期权让他达到我无法企及的富有程度（译注：指Jamie Zawinski，Xemacs和Netscape的作者）。
跟别的程序员一起完成项目。在一些项目中成为最好的程序员；在其他一些项目中当最差的一个。当你是最好的程序员时，你要测试自己领导项目的能力，并通过你的洞见鼓舞其他人。当你是最差的时候，你学习高手们在做些什么，以及他们不喜欢做什么（因为他们让你帮他们做那些事）。
接手别的程序员完成项目。用心理解别人编写的程序。看看在没有最初的程序员在场的时候理解和修改程序需要些什么。想一想怎样设计你的程序才能让别人接手维护你的程序时更容易一些。
学会至少半打编程语言。包括一门支持类抽象（class abstraction）的语言（如Java或C++），一门支持函数抽象（functional abstraction）的语言（如Lisp或ML），一门支持句法抽象（syntactic abstraction）的语言（如Lisp），一门支持说明性规约（declarative specification）的语言（如Prolog或C++模版），一门支持协程（coroutine）的语言（如Icon或Scheme），以及一门支持并行处理（parallelism）的语言（如Sisal）。
记住在“计算机科学”这个词组里包含“计算机”这个词。了解你的计算机执行一条指令要多长时间，从内存中取一个word要多长时间（包括缓存命中和未命中的情况），从磁盘上读取连续的数据要多长时间，定位到磁盘上的新位置又要多长时间。（答案在这里）
尝试参与到一项语言标准化工作中。可以是ANSI C++委员会，也可以是决定自己团队的编码风格到底采用2个空格的缩进还是4个。不论是哪一种，你都可以学到在这门语言中到底人们喜欢些什么，他们有多喜欢，甚至有可能稍微了解为什么他们会有这样的感觉。
拥有尽快从语言标准化工作中抽身的良好判断力。
抱着这些想法，我很怀疑从书上到底能学到多少东西。在我第一个孩子出生前，我读完了所有“怎样……”的书，却仍然感到自己是个茫无头绪的新手。30个月后，我第二个孩子出生的时候，我重新拿起那些书来复习了吗？不。相反，我依靠我自己的经验，结果比专家写的几千页东西更有用更靠得住。

Fred Brooks在他的短文《No Silver Bullets》（没有银弹）中确立了如何发现杰出的软件设计者的三步规划：

尽早系统地识别出最好的设计者群体。
指派一个事业上的导师负责有潜质的对象的发展，小心地帮他保持职业生涯的履历。
让成长中的设计师们有机会互相影响，互相激励。
这实际上是假定了有些人本身就具有成为杰出设计师的必要潜质；要做的只是引导他们前进。Alan Perlis说得更简洁：“每个人都可以被教授如何雕塑；而对米开朗基罗来说，能教给他的倒是怎样能够不去雕塑。杰出的程序员也一样”。

所以尽管去买那些Java书；你很可能会从中找到些用处。但你的生活，或者你作为程序员的真正的专业技术，并不会因此在24小时、24天甚至24个月内发生真正的变化。

（全文完）

Teach Yourself Programming in Ten Years

Peter Norvig

Why is everyone in such a rush?

Walk into any bookstore, and you’ll see how to Teach Yourself Java in 7 Days alongside endless variations offering to teach Visual Basic, Windows, the Internet, and so on in a few days or hours. I did the following power search at Amazon.com:
pubdate: after 1992 and title: days and
(title: learn or title: teach yourself)
and got back 248 hits. The first 78 were computer books (number 79 was Learn Bengali in 30 days). I replaced “days” with “hours” and got remarkably similar results: 253 more books, with 77 computer books followed by Teach Yourself Grammar and Style in 24 Hours at number 78. Out of the top 200 total, 96% were computer books.
The conclusion is that either people are in a big rush to learn about computers, or that computers are somehow fabulously easier to learn than anything else. There are no books on how to learn Beethoven, or Quantum Physics, or even Dog Grooming in a few days. Felleisen et al. give a nod to this trend in their book How to Design Programs, when they say “Bad programming is easy. Idiots can learn it in 21 days, even if they are dummies.

Let’s analyze what a title like Learn C++ in Three Days could mean:

Learn: In 3 days you won’t have time to write several significant programs, and learn from your successes and failures with them. You won’t have time to work with an experienced programmer and understand what it is like to live in a C++ environment. In short, you won’t have time to learn much. So the book can only be talking about a superficial familiarity, not a deep understanding. As Alexander Pope said, a little learning is a dangerous thing.
C++: In 3 days you might be able to learn some of the syntax of C++ (if you already know another language), but you couldn’t learn much about how to use the language. In short, if you were, say, a Basic programmer, you could learn to write programs in the style of Basic using C++ syntax, but you couldn’t learn what C++ is actually good (and bad) for. So what’s the point? Alan Perlis once said: “A language that doesn’t affect the way you think about programming, is not worth knowing”. One possible point is that you have to learn a tiny bit of C++ (or more likely, something like JavaScript or Flash’s Flex) because you need to interface with an existing tool to accomplish a specific task. But then you’re not learning how to program; you’re learning to accomplish that task.
in Three Days: Unfortunately, this is not enough, as the next section shows.
Teach Yourself Programming in Ten Years

Researchers (Bloom (1985), Bryan & Harter (1899), Hayes (1989), Simmon & Chase (1973)) have shown it takes about ten years to develop expertise in any of a wide variety of areas, including chess playing, music composition, telegraph operation, painting, piano playing, swimming, tennis, and research in neuropsychology and topology. The key is deliberative practice: not just doing it again and again, but challenging yourself with a task that is just beyond your current ability, trying it, analyzing your performance while and after doing it, and correcting any mistakes. Then repeat. And repeat again. 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. Malcolm Gladwell reports that a study of students at the Berlin Academy of Music compared the top, middle, and bottom third of the class and asked them how much they had practiced:
Everyone, from all three groups, started playing at roughly the same time – around the age of five. In those first few years, everyone practised roughly the same amount – about two or three hours a week. But around the age of eight real differences started to emerge. The students who would end up as the best in their class began to practise more than everyone else: six hours a week by age nine, eight by age 12, 16 a week by age 14, and up and up, until by the age of 20 they were practising well over 30 hours a week. By the age of 20, the elite performers had all totalled 10,000 hours of practice over the course of their lives. The merely good students had totalled, by contrast, 8,000 hours, and the future music teachers just over 4,000 hours.
So it may be that 10,000 hours, not 10 years, is the magic number. (Henri Cartier-Bresson (1908-2004) said “Your first 10,000 photographs are your worst,” but he shot more than one an hour.) Samuel Johnson (1709-1784) thought it took even longer: “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 (1340-1400) complained “the lyf so short, the craft so long to lerne.” Hippocrates (c. 400BC) is known for the excerpt “ars longa, vita brevis”, which is part of the longer quotation “Ars longa, vita brevis, occasio praeceps, experimentum periculosum, iudicium difficile”, which in English renders as “Life is short, [the] craft long, opportunity fleeting, experiment treacherous, judgment difficult.” Although in Latin, ars can mean either art or craft, in the original Greek the word “techne” can only mean “skill”, not “art”.

So You Want to be a Programmer

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 your ten years/10,000 hours.
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.
Talk with other programmers; read other programs. This is more important than any book or training course.
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 your own or 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 made enough in stock options to buy his own nightclub.
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. Understand 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 them 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 Bullet identified a three-part plan for finding great software designers:

Systematically identify top designers as early as possible.
Assign a career mentor to be responsible for the development of the prospect and carefully keep a career file.
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”. Perlis is saying that the greats have some internal quality that transcends their training. But where does the quality come from? Is it innate? Or do they develop it through diligence? As Auguste Gusteau (the fictional chef in Ratatouille) puts it, “anyone can cook, but only the fearless can be great.” I think of it more as willingness to devote a large portion of one’s life to deliberative practice. But maybe fearless is a way to summarize that. Or, as Gusteau’s critic, Anton Ego, says: “Not everyone can become a great artist, but a great artist can come from anywhere.”
So go ahead and buy that Java/Ruby/Javascript/PHP 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 weeks. How about working hard to continually improve over 24 months? Well, now you’re starting to get somewhere…

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.

Bryan, W.L. & Harter, N. “Studies on the telegraphic language: The acquisition of a hierarchy of habits. Psychology Review, 1899, 8, 345-375

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

Chase, William G. & Simon, Herbert A. “Perception in Chess” Cognitive Psychology, 1973, 4, 55-81.

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

Answers

Approximate timing for various operations on a typical PC:
execute typical instruction 1/1,000,000,000 sec = 1 nanosec
fetch from L1 cache memory 0.5 nanosec
branch misprediction 5 nanosec
fetch from L2 cache memory 7 nanosec
Mutex lock/unlock 25 nanosec
fetch from main memory 100 nanosec
send 2K bytes over 1Gbps network 20,000 nanosec
read 1MB sequentially from memory 250,000 nanosec
fetch from new disk location (seek) 8,000,000 nanosec
read 1MB sequentially from disk 20,000,000 nanosec
send packet US to Europe and back 150 milliseconds = 150,000,000 nanosec
Appendix: Language Choice

Several people have asked what programming language they should learn first. There is no one answer, but consider these points:
Use your friends. When asked “what operating system should I use, Windows, Unix, or Mac?”, my answer is usually: “use whatever your friends use.” The advantage you get from learning from your friends will offset any intrinsic difference between OS, or between programming languages. Also consider your future friends: the community of programmers that you will be a part of if you continue. Does your chosen language have a large growing community or a small dying one? Are there books, web sites, and online forums to get answers from? Do you like the people in those forums?
Keep it simple. Programming languages such as C++ and Java are designed for professional development by large teams of experienced programmers who are concerned about the run-time efficiency of their code. As a result, these languages have complicated parts designed for these circumstances. You’re concerned with learning to program. You don’t need that complication. You want a language that was designed to be easy to learn and remember by a single new programmer.
Play. Which way would you rather learn to play the piano: the normal, interactive way, in which you hear each note as soon as you hit a key, or “batch” mode, in which you only hear the notes after you finish a whole song? Clearly, interactive mode makes learning easier for the piano, and also for programming. Insist on a language with an interactive mode and use it.
Given these criteria, my recommendations for a first programming language would be Python or Scheme. But your circumstances may vary, and there are other good choices. If your age is a single-digit, you might prefer Alice or Squeak (older learners might also enjoy these). The important thing is that you choose and get started.
Appendix: Books and Other Resources

Several people have asked what books and web pages they should learn from. I repeat that “book learning alone won’t be enough” but I can recommend the following:
Scheme: Structure and Interpretation of Computer Programs (Abelson & Sussman) is probably the best introduction to computer science, and it does teach programming as a way of understanding the computer science. You can see online videos of lectures on this book, as well as the complete text online. The book is challenging and will weed out some people who perhaps could be successful with another approach.
Scheme: How to Design Programs (Felleisen et al.) is one of the best books on how to actually design programs in an elegant and functional way.
Python: Python Programming: An Intro to CS (Zelle) is a good introduction using Python.
Python: Several online tutorials are available at Python.org.
Oz: Concepts, Techniques, and Models of Computer Programming (Van Roy & Haridi) is seen by some as the modern-day successor to Abelson & Sussman. It is a tour through the big ideas of programming, covering a wider range than Abelson & Sussman while being perhaps easier to read and follow. It uses a language, Oz, that is not widely known but serves as a basis for learning other languages. <

重读王若度《关于爱好和生活》及佘头《武道释义》

——————————————记住在LSM静心快乐读书的那种感觉~

“如果从学术的角度来讲，其实我玩游戏并不划算。学术界是真真正正靠实力来说话的。当然，我并不后悔，因为我们来到这个世上，本质上是来做一个人的，是来生活的，而不是来做学术的、不是来成功的、也不是来挣钱的、更不是来取悦什么人的，那些都应是为生活本身而服务的。如果你不快乐，不精神满足，不能放声大笑夜夜孤枕难眠，那你就是腰缠万贯，得了诺贝尔奖又有什么意义呢。

大家应当寻找属于自己的、有益的爱好，以及自己事业和生活的目标。寻找生活的目标是一个长期的、不断更新维护的并不断享受的过程。我希望：当某一天突然发现自己应当放弃自己长期以来追求的、以为是自己今生的目标的事情，而去追求别的、即使在他人眼中看来毫无意义的事情的时候，迈得出那一步。”

截拳道，其内在的自由无技的特点和以无法为有法，以无形为有形，以无限为有限的启蒙纲领，自成一套以诚实的自我表达为宗旨和最高境界的哲学体系。对任何一个人，其搏击的形式取决于其四肢和身体的综合协调发挥所表现的外在特点，这是因为我们的生理构造决定了我们的打斗方式。而在纷繁复杂的格斗中，情形往往瞬息万变，如果以固定不变的形来应付千变万化的实际情况，那么得到的必定是失败。道家哲学里讲：人法地，地法天，天法道，道法自然。所以最直接，最迅速，最简洁的攻击往往最有效。一个好的武术家，应该能洞察一切，将简单直接的技法练到条件反射，以致肌肉对练习的记忆足够唤起在实战中瞬间快速灵活的反应。武术乃至运动的极致，都是为了两点：速度和力量。不管是内家拳还是外家拳，其要称之为武则都要达到至高的速度和力量，不然就不会产生最够大的杀伤力。不能用于挑战自我极限或实战的武术，充其量只不过是形式的招式，正所谓“练武不练功，到老一场空。”武术究其源泉是一种搏击的技术，但如果把武术单纯理解为打架或者暴力，并滥用武力，则远远偏离了武学的本质所在；也有人把武术高深的境界片面理解为修内，也就是自我挑战能力的不断提升，强调武学的儒家和道家思想，这固然是至关重要的一点，但如果忽视了搏击性，则一样不能称其为武学，因为这样永远也不会真正在武术的意义上全面提升自我并突破自我能力的极限，达到化境。缺乏乃至没有搏击性的武术最多只是武学基础上产生的艺术，而不会是武术本身。真正的武术，必然融合了其搏击的本质与指导其发展的哲学，两者相互促进，相互协调，缺一不可。

武术的每招每式，不论派别，不论功法，都有其必然的搏击性在里面。而宗其真意，无非攻防二字。而攻击与防守本身的哲学就在八卦的阴阳鱼中有着深刻的体现。攻击的一刹那，也是自己防守最薄弱的时候；防守的一刹那，也是攻击的最佳时机。阴阳互易，而越是好的武术家，则越能在这种互易中寻求自我状况的和谐与稳定，从而不仅把握了搏击的胜负，而且在自我能力的控制与发挥上做到游刃有余。要寻求做到这两点，则需要自我最诚实的表达，而这却是非常困难的。
单纯从武术的搏击性来讲，又可以把攻击的要素简化为近身和发力，把防守孕育在攻击之中。我们身体的构造决定了我们必须通过近身来接近对方，通过发力来打倒对手。所以李小龙早就说过：“搏击的精义就是移动的技巧”，近身和发力永远是任何搏击术所必须遵循的原则，否则不可能打倒对手。这两点上，就必须通过长时间的刻苦训练来寻求身体的协调性与灵活性，速度和力量的训练以致达到条件反射就变得格外重要。因为实战中对手不可能告诉你他会怎么打，对于瞬间发生的情况的反应往往直接决定胜负。谈到防守，则必须将攻击和其相互结合。因为攻击和防守的先后次序总会给对手较大的时间差去反应，不利于搏击取得胜利。攻防结合的打法不仅可以削减对方的攻击，而且不易让自己受到重创。咏春拳就是这种打法的典范。截拳道正是吸收并改进了咏春的打法，让其本身变得更加实用。而这一技法的成功应用，则在于平时训练的习惯和方式。
武术，虽然是一种武装的力量，然而真正的武术，则是融入了武学多少年发展的哲理和武术家个人的修为。决不是简简单单的打斗对抗，或者甚至是打架。不同的哲学体系下会产生风格迥异的武术，使武术在不同的民族，不同的国家和不同的历史背景下有着不同的意义。武术本身可以作为一种文化加以传播和交流，从而得以发展，同时也可以作为自我挑战和提升的手段。但从哲学上看，武术是我们不同的个体认知和体悟这个世界的很好的途径。我们所有的科学或者哲学，都是在我们的认知层面上产生的物质乃至精神上的进步。武术也是如此，只不过换了一种方式。
武道对于我本身，则是通向精神自由和自我真诚表达的一扇门。