荔枝派Zero（V3s）制作SPI Flash 系统镜像时使用的最小根文件系统

该程序是看了网上一篇论文后进行了复现包括了基础复现和原文复现（原文复现使用了pytorch框架）在该资源包中附带复现的论文，语义空间矩阵，相关程序，还有数据集的相关说明（由于数据集过大，请自行下载数据集）

最强人工智能围棋程序，秒杀Zen7，CPU版本，无需显卡即可运行，支持64位Win系统。需要通过Sabaki加载Leela Zero引擎实现对弈。Sabaki是一款开源的围棋对弈及打谱软件，界面非常优雅漂亮。Sabaki下载地址https://github.com/SabakiHQ/Sabaki/releases

解决androidstudio中出现finished with non-zero exit value 1，或者finished with non-zero exit value 2的问题

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.

DeepMind公布了AlphaGo的最新升级版本AlphaGo Zero，并于最新一期的《自然》杂志上，对其使用的相应技术做出详解。 DeepMind称，“AlphaGo Zero与AlphaGo最大的不同是做到了真正的自我学习，经过3天的训练，就以100：0的战绩完胜前代AlphaGo。”