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我也要
一、下载最新版的 Win10 镜像
1、打开 MSDN 下载 Windows 系统镜像，即 https://msdn.itellyou.cn/
2、下载的 iso 镜像文件
3、你可以通过双击 iso 镜像文件，点击 setup.exe 进行在线安装
4、但是，我发现通过这种方式安装的系统（可以用），然而，C:/ D:/ E:/ ...盘符内所有的文件都还在，对于我这个强迫症来说，这是不允许的。当然，可以根据个人需求进行操作（你也可以试试在线安装，也许是我电脑的问题也说不定呢）
注：MSDN 所下载的系统为纯净的原版系统（厂商 Win10家庭版 用起来束手束脚，正所谓：你不让我嘿嘿嘿，我就...，故...）
二、制作 PE系统盘
1、打开 微PE 下载 PE工具箱，即 http://www.wepe.com.cn/
2、点击 下载，进入下载页面，选择 64位 的 Win10PE工具箱 进行下载
3、找到并双击打开刚才下载的 WeP
Win10+Ubuntu双系统安装教程
安装时间：2016年1月初。安装版本：ubuntu优麒麟15.10。
Win11+Ubuntu20.04双系统安装流程
Win11+Ubuntu20.04双系统安装流程
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ABAP XCO库子类枚举技术解析 本文介绍了在ABAP项目中如何利用XCO库高效获取类的继承层次结构。传统方法使用CL_ABAP_CLASSDESCR或手动维护映射表存在效率低、易遗漏的问题。新方法通过XCO库的IF_XCO_AO_CLASS接口，可简洁地获取父类的所有子类（包括间接子类）。 核心API链式调用： 获取类句柄：xco_cp_abap=>class() 导航子类集合：subclasses->all 获取结果：get()返回类引用表或get_names()返回类名列表 典型应用场景
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【论文解读--Xdog】新型电驱式四足机器人研制与测试
系列文章目录 提示：这里可以添加系列文章的所有文章的目录，目录需要自己手动添加 TODO:写完再整理 文章目录系列文章目录前言一、四足机器人体型大小和驱动方式的比较二、四足机器人设计一些重要的结论三、系统控制框架四、系统硬件框架五、力位混合控制（阻抗控制）六、侧向平衡控制（和VMC如出一辙）七、电机驱动系统研制（FOC磁场定向控制）总结 前言 认知有限，望大家多多包涵，有什么问题也希望能够与大家多交流，共同成长！ 本文先对新型电驱式四足机器人研制与测试做个简单的介绍，具体内容后续再更，其他模块可以参
uniapp [全端兼容] - 详细实现下拉筛选菜单功能组件，下拉式筛选菜单效果，类似电商美团点击筛选项底部弹出列表点击选择后自动收起（详细示例代码，全端兼容小程序、安卓苹果app、h5网页、nvue
uni-app，vue2，vue3，向下弹出菜单包含筛选功能，日期筛选，下拉框选择器，下拉多条件筛选，下拉式筛选菜单，uniApp如何实现条件下拉筛选，Uniapp官方条件筛选组件，下拉框式条件筛选插件组件，最好用的插件组件，多端兼容，下拉菜单筛选，过滤筛选列表，下拉筛选栏，多列联动的条件筛选，下拉菜单地区和筛选，微信小程序，支付宝小程序，h5网页，移动端网站，安卓app，苹果app，nvue，uniapp所有平台兼容，下拉菜单筛选，下拉式条件筛选，下拉列表，点击排序，级联筛选，uniapp实现下拉筛选
Ros入门 （十）----激光雷达避障 简易实现
本篇文章通过订阅/scan话题获取障碍物的距离信息，达到避障目的，提供部分代码，仅供参考。
Unity之OpenXR+XR Interaction Toolkit接入HTC Vive
目前VR领域，最流行的设备要当属HTC VIVE了。以前在Unity端接入HTC VIVE设备时，都是通过SteamVR+VRTK来接入的。但是随着Unity的版本升级和OpenXR标准的流行，再加上VR设备的逐渐增多。SteamVR+VRTK的方式已经无法更好的满足大家的需求。
最新的sdio 4.0 spec 协议标准 带书签 高清完整PDF版
Part-E1-SDIO-Specification-Ver4.00-Final-120220.pdf：最新的sdio 4.0 spec 协议标准 带书签 高清完整PDF版，包括SDIO 接口的外设，SDIO 接口官方协议等。
大中型超市收银系统(可直接运行)
光步前台收银系统系统功能：商品销售、商品退货、挂单、收银员权限、会员销售、单品取消、整单取消、商品查询、脱机销售、脱机数据上传，基本资料下载，开钱箱、小票打印、收款结算，支持多币种付款方式（现金、储值卡、礼券、微信等）,支持多种促销。运行环境：.net 4.0登陆信息用户名:1001 密码:123456内置商品信息，商品编码可输入00001000 - 00010000,测试效果相关功能键F5 结算 F9挂单 F11商品查询 F8退货 Insert单品取消 Del整单取消 F12开钱箱F2 会员 Ctrl+Z退出系统 Home 锁屏 相关源码待公开
WINCC7.3破解文件
WINCC7.3硬件够破解文件，有需要的下载！WINCC7.3硬件够破解文件，有需要的下载！
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
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