原文:http://debuggable.com/posts/learning-from-the-cakephp-source-code-part-ii:480f4dd6-57fc-4715-8709-439acbdd56cb 这段评论有意思: 非常正确,确实应该从代码入手! Hey Felix! As you wrote in Part I most of Cake's core code is "delightful and should help you to become a bette…

最近开始痛定思痛,研究cakephp的源码. 成长的路上从来没有捷径,没有小聪明. 只有傻傻的努力,你才能听到到成长的声音. 下面这篇文章虽然过时了,但是还是可以看到作者的精神,仿佛与作者隔着时空的交流,这就是阅读的意义所在吧 :) ============================================================= 原文: http://debuggable.com/posts/learning-from-the-cakephp-source-code-p…

英语在软件行业的重要作用不言自明,尤其是做国际项目和写国际软件,好的英语表达是项目顺利进行的必要条件.纵观眼下的IT行业.可以流利的与国外客户英文口语交流的程序猿占比并非非常高.要想去国际接轨,语言这一关一定要过. 本人做刚入行的时候非常想找一本专门写给程序猿的英文教材,但并没有找到特别合适的.通过这几年的欧美项目经理,我发现与国外同行交流重在表明交流的意图而轻语法规定.一件事情的表述,仅仅要可以用几个Key Words来表述清楚.两方可以理解就可以.并没有使用我们上学期间艰深晦涩的语法知识.…

这一次想把标点符号的英语表达总结一下,这些单词非常重要但easy被我们忽视.以我的经验,还是多认识几个.以备不时之需. 以下从"标点符号"開始: punctuation [英][ˌpʌŋktʃuˈeɪʃn][美][ˌpʌŋktʃuˈeʃən] n.标点法; 标点符号; 标点符号的使用; 点标点; ampersand[英][ˈæmpəsænd][美][ˈæmpərsænd]n."&"的记号名称,and符; 源代码中使用: 出自frameworks/base/c…

收藏一些经典的源码,持续更新!!! 1.深度学习框架(Deep Learning Framework). A:Caffe (Convolutional Architecture for Fast Feature Embedding)Convolutional 由伯克利大学Yangqing Jia Ph.D开发的开源深度学习的代码. Homepage:http://caffe.berkeleyvision.org/ Paper:Caffe: Convolutional Architecture f…

/* The Quest Operating System * Copyright (C) 2005-2010 Richard West, Boston University * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Softwar…

不废话,直接上代码, 先看截图use pictures;…

In the previous post we addressed some issue of decision tree, including instability, lack of smoothness, sensitivity to data, and etc. One solution is Boosting Method. In simple words Boosting combines multiple weak learners to get a powerful predic…

After talking about Information theory, now let's come to one of its application - Decision Tree! Nowadays, in terms of prediction power, there are many ensemble methods based on tree that can beat Decision Tree generally. However I found it necessar…

This is the second post in Boosting algorithm. In the previous post, we go through the earliest Boosting algorithm - AdaBoost, which is actually an approximation of exponential loss via additive stage-forward modelling. What if we want to choose othe…