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我也要
开发环境：win10+python3.7
开发工具：pycharm
一想到远程，就想到创建连接，一想到创建链接，就想到socket套接字（一想到套接字，就联想到我没有男盆友）
分别为三个知识点：
1.socket远程连接
2.创建客服端和服务端
3.管理员权限运行代码
咱们今天是学习修改电脑密码和远程修改小伙伴的密码，如果有哪里解析不好，欢迎大佬指点一下。
该项目代码为3份（记住自己拿的是服务端的代码，客服端代码和go.cmd是发给别人的）
首先正常创建服务端：
创建套接字
绑定ip端口
设置监听
等待连接
接收数据打印数据
关闭连接
import socket # 导入socket
server = socket.socket(socket.AF_INET, socket.SOCK_STREAM) # 创建socket
server.bind(('192.168.246.1', 44444)) # 绑定IP/端口
server.listen(5) # 监听
print('***********等待连接*新手避坑指南：用SARScape 5.6.2处理Sentinel-1数据做SBAS-InSAR，这些参数设置错了白忙活
本文详细介绍了使用SARScape 5.6.2处理Sentinel-1数据做SBAS-InSAR时的关键参数设置技巧，帮助新手避免常见错误。重点解析了连接图生成、干涉工作流、DEM处理等环节的12个关键参数，包括Max Normal Baseline、Unwrapping Coherence Threshold等，提供实测安全值范围和优化建议，确保处理效率和结果准确性。
[Linux外设驱动详解]RK3588 U-Boot网络功能深度分析
本文深入分析了RK3588平台上U-Boot的网络功能实现，主要内容包括： 网络架构：详细剖析了从硬件层到协议层的完整网络栈架构，包括GMAC驱动、TFTP/NFS启动、网络命令等关键组件。 驱动实现：重点介绍了RK3588 GMAC驱动结构，包含关键数据结构、专用操作函数集、设备树兼容性配置和GRF寄存器设置。 功能实现：详细说明了驱动初始化流程、RGMII速度适配机制以及PHY接口配置过程。 高级特性：涵盖了网络启动功能、镜像更新和MAC地址传递到Linux内核的机制。 文档通过代码片段和流程图清晰展示
中文实体关系抽取数据集大盘点：从百度Duie2.0到医疗CBLUE，哪个更适合你的NLP项目？
本文全面分析了中文实体关系抽取领域的主流数据集，包括百度DuIE 2.0、医疗CBLUE等，帮助开发者根据项目需求选择最适合的数据集。文章详细对比了各数据集的特点、适用场景及优化策略，为自然语言处理项目提供实用指南。
手把手教你编写I2C读写EEPROM代码（驱动层实现）
从底层寄存器配置到时序控制，详解如何编写可移植的I2C读写EEPROM代码，覆盖起始信号、地址帧、数据收发及ACK应答等关键环节，确保在STM32、ESP32等主流平台稳定运行，真正落地i2c读写eeprom代码需求。
WVP+ZLM国标平台实战：从设备注册失败到视频播放黑屏，我遇到的5个典型问题与解法
部署WVP+ZLM国标视频平台时，设备注册失败、视频播放黑屏等问题常常让运维人员头疼不已。本文将基于真实运维场景，剖析五个最具代表性的故障案例，提供从网络配置到版本兼容性的全链路解决方案。
使用FileZilla在武汉大学IGS数据分析中心下载GNSS数据
2.依次点击如下图标下载到本地并解压安装（按系统选择要下载的版本）
nuScenes数据集详细介绍
nuScenes网上介绍资料较多，但是大部分仅仅是对官网的翻译，缺乏各个文件的内在联系介绍。例如，nuScenes激光雷达的数据格式，点云包含哪几种属性。再比如，sample文件夹和sweeps文件夹下的文件关系，以及是如何在相关json文件中体现的。本文将详细介绍nuScenes数据集的各个部分。
GEE：基于MODIS数据计算NDVI时间序列，并使用Savatsky-Golay（SG）滤波器对其进行平滑处理
优快云@_养乐多_本文将介绍如何在 Google Earth Engine (GEE) 平台中，使用 MODIS 数据计算 NDVI 时间序列，并对该数据集应用 Savitzky-Golay 滤波器，得到了一个新的平滑后的数据集。Savitzky-Golay 滤波器可以有效地去除信号中的噪声，并平滑信号的变化。该方法可以扩展到基于 MODIS 数据计算的任何指数或者波段。
OV9734 DATASHEET，完整规格书
OV9734完整规格书，包括电气参数，尺寸规格，寄存器配置表等
android 蓝牙通讯demo
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计算机控制技术及应用
《计算机控制技术及应用》通过总结作者的科研成果和吸收国内外的先进理论、方法和技术，以计算机控制系统的网络化、开放化、智能化和集成化发展趋势为主线索，主要介绍计算机控制系统的基本概念和计算机控制系统的典型形式，计算机控制系统的数学描述与基本分析方法，以及数字PID控制器、最少拍控制系统、达林算法、状态变量设计法等设计方法及其各种改进算法。重点突出计算机控制系统输入输出通道接口的设计方法和典型控制系统的设计案例与实现方法等。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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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