recurrent neural network without a phd.pdf 出处：https://github.com/martin-gorner/tensorflow-rnn-shakespeare

[无泪的拓扑].Topology_Without_Tears.pdf

文件包括Active Contours Without Edges的PDF文档已经相对应的代码另外还有本人翻译的部分文档，当然翻译的不是很好，可以看一下，还有原文章的大部分数学公式。

乍一看这本书的名字，Expert one on one J2EE development without EJB并没有给人带来太冲击。毕竟关于J2EE的书太多了，而without EJB看上去有点象是故意挑衅EJB的感觉。一本J2EE的书怎么可能会给人带来信念或思维的冲击呢？但是它做到了，它不仅使自己变成了不朽的经典，也使Rod Johnson成为了我最近一年的新偶像。 　　　　　　　　　　　　　　　　　　　　　　　　--xiecc 　　你的J2EE项目是否耗费了你太多的时间？它们是否难以调试？它们是否效率不彰？也许你还在使用传统的J2EE方案，然而这种主案太过复杂，而且并非真正面向对象。这里的很多问题都与EJB有关：EJB是一种复杂的技术，但它没有兑现自己曾经的承诺。

讲述alpha zero的原文，发表在nature。 A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo.