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Node.js是一个基于Chrome V8引擎的JavaScript运行时环境，它允许开发者在服务器端运行JavaScript代码。本文将为您提供在Linux系统下安装和部署Node.js的详细步骤。
步骤1：更新系统
在开始安装Node.js之前，首先确保您的Linux系统处于最新状态。打开终端并执行以下命令来更新系统：
sudo apt update
sudo apt upgrade
步骤2：安装Node.js
在Linux上安装Node.js有多种方法，本文将介绍使用Node.js版本管理工具nvm进行安装的方法。nvm允许您轻松地在不同的Node.js版本之间切换。
2.1 安装nvm
首先，使用以下命令下载并安装nvm：
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.38.0/install.sh | bash
</Linux环境下安装Node.js及npm
npm是Node.js的软件包管理器，用于安装、管理和分享代码模块。接下来，我们将使用Node.js的官方包管理器（NodeSource）来安装Node.js。至此，您已经成功在Linux系统中安装了Node.js和npm。Node.js的安装包中已经包含了npm，因此您无需单独安装。在安装Node.js之前，首先需要更新系统的软件包列表。本文将详细介绍如何在Linux系统中安装Node.js和npm，并提供相应的源代码。如果成功安装，您将看到Node.js的版本号。如果成功安装，您将看到npm的版本号。
linux下的node.js下载和安装（三）
本文提供了Linux系统下Node.js的快速部署指南，主要包含以下步骤：1）通过wget或上传方式获取Node.js安装包（示例版本v12.14.0）；2）使用xz和tar命令解压安装包；3）创建node/npm的软连接实现全局调用；4）配置淘宝npm镜像加速下载（可选）；5）通过环境变量配置标准化管理（修改/etc/profile并设置NODE_HOME及PATH）。最后通过node -v和npm -v验证安装，并强调配置需通过source命令即时生效。全文涵盖从下载到验证的完整流程，适合运维人员和初学
vuecli3 实现 移动端和pc端 界面切换（两套代码）
适合场景 多页面多系统应用 所有系统都在同一目录下。配置多入口多出口。每个系统之间可以链接。每个系统内依然采用Vue单页应用开发。 产品需求：一套代码 兼容pc端和移动端 ， 移动端和pc端的样式布局差别很大，需要使用多界面，并判断使用手机的时候跳转移动端代码，使用pc端跳转pc端代码。 项目目录 分为两部分代码，pc 和mobile 第一步：创建一个登陆页面的文件 在项目public文件夹下创建一个login.html，其实就是将index.html复制一份，将title改一下： <!DOCTYP
华为ENSP完整安装教程
华为ENSP完整安装教程
在VS2019里把JSBSim飞行模型集成到你的C++项目里（附完整配置流程和常见错误解决）
本文详细介绍了如何在Visual Studio 2019中将JSBSim飞行模型集成到C++项目中，包括环境准备、源码编译、项目配置和常见错误解决方案。通过实战技巧和优化建议，帮助开发者高效实现高精度飞行仿真功能，提升开发效率。
保姆级拆解：HP 1312NF/CM1415打印机进纸离合器清洁与更换全记录（附B站/油管视频指路）
本文详细记录了HP 1312NF打印机进纸离合器故障的诊断与修复全过程，包括故障判断、工具准备、拆解步骤、清洁更换技巧及预防措施。特别针对离合器内部海绵老化粘连问题，提供了电工胶布和硬纸片两种实用替代方案，并附有B站/油管视频指路，帮助用户轻松完成打印机维修。
前端build工程的时候 出现 `build.rollupOptions.external 的解决方法
看到这个Bug，理性分析： 遇到 Rollup failed to resolve import “@vue/devtools-api” 错误可能是由于 Pinia 使用了 @vue/devtools-api 模块，而 Vite 的 Rollup 插件无法解析这个模块 ps：但是我前一秒还可以，加了个别代码，突然不行，我是不相信的 = - =
车辆网络安全ISO/SAE 21434解读（一）概述
ISO/SAE 21434 《道路车辆 网络安全工程》是SAE和ISO共同制定的一项针对道路车辆的网络安全标准，它是一个面向汽车行业全供应链（OEM及各级供应商）的车辆网络安全管理指导文件
【Claude】Invalid model / Opus not available 错误：模型不可用的原因 bug报错已解决
使用 Claude API 时，指定错误或不可用的模型 ID 会收到 Invalid model 错误。本文从模型命名规则出发，给出模型降级链与动态选择方案。
2xmax96717+max96724+agx orin驱动代码
说明：这里主要解决max96717的设备地址和sensor地址映射，vc0->vc1多颗max96717+max96724使用配置思路如下： 1、改变max96717的pipez stream id，然后max96724的pipe绑定使用相应的stream id 2、max96717默认是vc0，把vc0改写成vc1\vc2\vc3，然后max96724再重新映射vc输出
IEM模型，地表粗糙度模拟
利用微波反演土壤水分时候，利用IEM模型可以实现对随机地表的模拟
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")
efm32 入门.pdf
本PDF共计22页，介绍的是操作驱动API，下载与否自己斟酌。
Devexpress16.2.6破解工具
Devexpress16.2.6破解工具Devexpress16.2.6破解工具Devexpress16.2.6破解工具Devexpress16.2.6破解工具
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