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我也要
电脑win8，想装个linux，写程序需要。因为双系统切换麻烦，用虚拟机会方便，不过运行效率不高。我只是跑一些小程序，所以虚拟机刚好满足。
我选择用VMware装ubuntu。
VMware官网（我下的是workstation player）：https://www.vmware.com/cn.html
ubuntu官网（最新16.04）：http://www.ubuntu.com/
安装过程参考：http://jingyan.baidu.com/article/14bd256e0ca52ebb6d26129c.html
先写个C++程序试试：
这个版本自带gcc编译器，但是还要配置头文件。在terminal里输入
在windows11安装VMware & ubuntu
在windows11安装VMware & ubuntu
使用VMware安装Ubuntu18.04
本篇旨在记录自己安装Ubuntu18.04过程，和遇到问题解决。
vmware ros 安装过程记录
VMware安装ROS过程记录
立创开源4G+GPS定位系统：基于微信小程序的低成本低功耗追踪方案
本文介绍了一个基于立创开源平台的超小型4G+GPS定位系统方案。该方案采用28mm*28mm主板，集成4G Cat.1通信与GPS定位，通过微信小程序“点灯专家”实现远程追踪管理，具备低成本、低功耗、易于部署的特点，适用于电动车、宠物、资产追踪等多种场景。
算法策略 | MACD跨周期短线交易策略开发(股指+商品双版)
量化策略开发，高质量社群，交易思路分享等相关内容
论文、报告中那些乱七八糟的图（甘特图、卡吉图，桑基图，小提琴图，弦图，螺旋图，风玫瑰图）
在一些论文或报告中，总是会看到一些熟悉的图，但作者总是不能好好说话，非得搞个洋名字、缩写字或者半洋半中的词来显示它多么高大上，有时候这种故弄玄虚、华而不实的写法也实在是让人看了不伦不类，图首先是让读者更容易看懂的，不是让你来故作高深、玩玄学的。
用LTspice仿真CMOS反相器功耗？这份避坑指南能省你3天调试时间
本文详细介绍了如何使用LTspice准确仿真CMOS反相器的功耗，包括动态功耗、短路功耗和静态功耗的测量技巧。通过工程实践指南，帮助电子工程师避开常见陷阱，优化仿真参数设置，提升仿真结果的可靠性，节省调试时间。
S32K344开发环境搭建全攻略：S32DS 3.5.12与RTD 3.0.0实战指南
在嵌入式开发中，集成开发环境（IDE）与实时驱动（RTD）的协同配置是项目启动的关键。S32DS作为NXP官方IDE，结合RTD包，为S32K3系列提供底层驱动支持。正确安装与路径配置能确保编译、调试与烧录流程顺畅，避免常见错误。该环境适用于汽车电子、工业控制等场景，如车身域控、BMS。本文基于S32K344芯片，详细记录从软件下载到调试的完整过程，帮助开发者快速搭建稳定开发环境。
【赛题解析&命令脚本配置】2023年全国职业院校技能大赛GZ073网络系统管理赛项赛题第10套模块A：网络构建 & (五)网络运维配置+(六)SDN网络配置-命令配置
【赛题解析&命令脚本配置】2023年全国职业院校技能大赛GZ073网络系统管理赛项赛题第10套模块A：网络构建 & (五)网络运维配置+(六)SDN网络配置-命令配置
37.华为路由器：loopback（本地回环）接口的作用
本地环回接口(或地址)，亦称回送地址(loopback address)。 此类接口是应用最为广泛的一种虚接口，几乎在每台路由器上都会使用。常见于如下用途： 1 作为一台路由器的管理地址 系统管理员完成网络规划之后，为了方便管理，会为每一台路由器创建一个loopback 接口，并在该接口上单独指定一个IP 地址作为管理地址 ，管理员会使用该地址对路由器远程登录（telnet ），该地址实际上起到了类似设备名称一类的功能。 但是通常每台路由器上存在众多接口和地址，为何不从当中随便挑选一个呢 ？ 原因如下：由于
你分得清ChatGPT、Gemini、Grok、豆包、千问、DeepSeek、OpenClaw、CloudCode、vibeCodeing、sKill、MCP,飞书、微信这些到底是什么吗？
AI技术生态呈现层级化发展，从基础模型到应用平台形成完整技术栈。核心分为五层：模型层（GPT、Gemini等基础模型）→AI助手层（ChatGPT等应用产品）→工具层（ClaudeCode等开发工具）→协议层（MCP等接口标准）→应用层（微信、飞书等平台）。未来趋势将形成"模型公司-开发框架-应用生态"三层结构，AI正逐步演变为新型操作系统，通过标准化协议连接各类外部系统，实现能力调用与生态整合。技术发展呈现出从底层基础设施到上层应用场景的垂直整合特征。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
老版QQ原生表情gif动态
这是老版的QQ表情，不过全是动态的的，Windows照片查看器是不会动的，可以右击用浏览器打开查看，再说一遍是老版的，只是分享给宁愿用老版也要动态的人。
TMS_WebGMaps_1.9.8.0_Full_Source
TMS_WebGMaps_1.9.8.0_Full_Source 全源码 delphi cbuilder
NTscan扫描工具
ntscan是一个扫描器，可以扫描没有密码和弱密码的共享，先到配置文件中填写猜测的用户名和密码，扫描过程中右侧出现的是扫描日志。
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