代码及报告都有 [问题描述] 　　已知n个字符在原文中出现的频率，求它们的哈夫曼编码。 [基本要求] 　　1. 初始化：从键盘读入n个字符，以及它们的权值，建立Huffman 树。（具体算法可参见教材P147的算法6.12） 　　2. 编码：根据建立的Huffman树，求每个字符的Huffman编码。 对给定的待编码字符序列进行编码。 [选作内容] 　　１. 译码：利用已经建立好的Huffman树，对上面的编码结果译码。 译码的过程是分解电文中的字符串，从根结点出发，按字符’0’和’1’确定找左孩子或右孩子，直至叶结点，便求得该子串相应的字符。 　4. 打印 Huffman树。 ［测试数据］ 利用教材P.148 例6－2中的数据调试程序。可设8种符号分别为A,B,C,D,E,F,G,H。编/译码序列为 “CFBABBFHGH”(也可自己设定数据进行测试)。

Nature资源，有关深度强化学习论文，可免费下载，资源共享

2017年B-Human的参赛报告和代码说明，包括系统架构和相关说明

ios9人机界面指南-中文版 本书译自 Apple 官方推出的设计指南《iOS Human Interface Guidelines》，完整（注意！是完整！满满都是泪啊！）翻译了该指南所有内容，在文字和排版上也尽可能保证忠实还原，但并未取得官方翻译授权。

Diffle-Human密钥交换 java平台实现 DH加密原理 生成的密钥存于文件 实习总结

这本书是人文通信（Human bond communication）的一本比较著名的书。希望大家可以从中学习到一些东西

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.