墨衍会员
·
AI 创作全网分发
墨衍智能分发
本文由作者通过墨衍一键同步至各平台
1分发平台
300+累计阅读
304平均单平台
已同步平台
优快云
微信公众号
微博
知乎
掘金
百家号
博客园
抖音
小红书
你的文章也可以这样分发
墨衍会员 ¥399起/年，写一次发全网
我也要
在Windows 10操作系统中，家庭组功能是一项非常实用的功能，可以方便地共享文件、打印机和其他资源。然而，有时候我们可能会遇到家庭组功能不见的问题。这篇文章将介绍如何通过编程来解决这个问题，并提供相应的源代码。
解决方案的核心思路是通过编程来重新启用家庭组功能。我们将使用Python编程语言和Windows API来实现这个目标。下面是详细的步骤：
步骤1：导入所需的库和模块
首先，我们需要导入所需的库和模块。在这个解决方案中，我们将使用ctypes库来调用Windows API。请确保您的系统中已经安装了Python，并且安装了ctypes库。
import ctypes
步骤2：定义函数来重新启用家庭组功能
接下来，我们需要定义一个函数来重新启用家庭组功能。我们将使用ctypes库中的函数来调用Windows API。以下是实现这一步骤的代码：
def enable_homegroup_feature()开启家庭组
intel (R) 7 Series Chipset Family SATA AHCI Controller
433M遥控器无线解码1527协议
433M遥控器无线解码1527协议，定时器轮询法
Unity动画状态机实战：Trigger和Bool参数到底怎么选？附5个常见场景解析
本文深入解析Unity动画状态机中Trigger和Bool参数的核心差异与应用场景。通过对比两者在描述瞬时事件与持续状态上的本质区别，并结合角色移动、攻击连招、跳跃落地、受击死亡及环境交互等5个实战场景，提供了清晰的选择逻辑与避坑指南，帮助开发者构建更健壮、易维护的动画逻辑。
STC51单片机串口多字节数据接收的优化策略
本文深入探讨了STC51单片机串口多字节数据接收的优化策略。针对数据丢包、粘包等常见痛点，核心思路是采用“缓冲队列+定时器超时”模式，将实时处理转变为“先收纳，后处理”。文章详细讲解了硬件初始化、中断服务程序优化（快速入队与激活超时裁判）以及主循环的非阻塞设计，并进阶介绍了环形缓冲区实现与错误处理，旨在构建稳定可靠的串口通信系统。
小程序设计中的区块链应用：数字藏品实现
本文旨在为开发者提供在小程序中实现区块链数字藏品功能的完整技术方案。区块链与数字藏品的基础概念智能合约的设计与实现小程序与区块链的交互方式数字藏品的完整实现流程文章将从基础概念入手，逐步深入到技术实现层面，最后通过完整项目案例展示数字藏品在小程序中的实现方式。每个技术点都会配合代码示例和架构图进行说明。区块链：一种分布式数据库技术，通过加密算法保证数据不可篡改数字藏品：基于区块链技术的数字资产，具有唯一性和可验证性NFT。
如何在飞腾平台上安装Ubuntu操作系统
注意： 若主板上配置的是NVME硬盘，在进行内核配置时，需要将内核的NEME驱动勾选上，否则移植内核时，无法找到文件系统，进不去操作系统。可以通过rufus软件将镜像做到U盘中，本次使用版本为Rufus 3.9.124 ，其他版本也可以。不要下载live版本的，没有准备网络环境，是一个头疼的事情，笔者暂时没有实现网络安装。将U盘接到主板usb口上，接上串口，显示器，，必需串口有打印才可以安装。可以通过dd命令，将iso安装到U盘。安装的是最小系统，缺少很多命令，需要联网更新，或则更换飞腾专用内核。
嵌入式工程师必学（52）：储能系统ESS
能量存储系统（ESS，Energystoragesystems）存在于各种各样的尺寸、形状和技术中。储能系统的技术，即基本的储能机制，自然会影响其重要特性，包括成本、安全性、性能、可靠性和寿命。然而，尽管基础技术很重要，但一个成功的储能项目依赖于对该技术的彻底和深思熟虑的实施，以实现项目的目标。成功的实施取决于储能系统的架构和组装情况。系统的体系结构可以决定其性能和可靠性，与它所使用的技术一致，甚至与之无关。
【ChangeCLIP】Remote sensing change detection with multimodal vision-language representation learning
ChangeCLIP论文及代码使用详解。持续更新变化检测最新论文，欢迎订阅本专栏。
解决com.alibaba.druid.pool.GetConnectionTimeoutException: wait millis 60027, active 20, maxActive 20错误
全网详细解决com.alibaba.druid.pool.GetConnectionTimeoutException: wait millis 60027, active 20, maxActive 20, creating 0, createErrorCount 24的错误
Probability and Statistics - 4th edition
Probability and Statistics - 4th edition
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
Data-Structures-and-Algorithm-Analysis-in-Java.pdf
作者: Weiss, Mark A. 出版社: Addison Wesley Data Structures and Algorithm Analysis in Java is an “advanced algorithms” book that fits between traditional CS2 and Algorithms Analysis courses. In the old ACM Curriculum Guidelines, this course was known as CS7. This text is for readers who want to learn good programming and algorithm analysis skills simultaneously so that they can develop such programs with the maximum amo...
复分析 阿尔福斯(Ahlfors) 第三版答案 英文
答案分为十章，有部分习题没有答案，全英文复分析阿尔福斯(Ahlfors) 第三版答案
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
订阅专栏 解锁全文
1万+
扫一扫
