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
100+累计阅读
69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
JavaScript是一种广泛应用于网页开发和后端服务器的脚本语言。深入了解JavaScript的关键概念和源代码示例对于准备面试的开发者来说至关重要。在本篇文章中，我将为你详细介绍一些常见的JavaScript面试题和相应的源代码示例。
function outerFunction() {
var outerVariable = 'I am outside!';
function innerFuOpen UI5 源代码解析之130：Configuration.js
sap.ui.core.Configuration.js 摘要 该文件是OpenUI5框架的核心配置文件，负责管理运行时环境的各种全局设置。随着架构演进，原先集中的配置功能已逐步拆分到更专业的模块（如Localization、Formatting等），当前文件主要承担兼容转发和事件桥接的角色： 功能定位： 保留旧API接口，内部转发到新模块 聚合多个配置源的变更事件 提供完整的弃用标注和迁移指引 关键能力： 语言/本地化配置 数字/日期格式化 主题管理 控件行为设置 安全策略 架构意义： 作为过渡层保证旧代
别再手动配电脑了！手把手教你用Windows Server 2022搭建企业AD域（附DNS配置避坑指南）
本文详细介绍了如何使用Windows Server 2022搭建企业AD域，提升20+终端的管理效率。通过实战案例和避坑指南，解析AD域的核心价值、部署步骤及DNS配置技巧，帮助企业实现集中权限管理、安全策略统一部署，显著降低IT运维成本。
CoppeliaSim远程API实战：用Python控制仿真机器人，5步搞定外部通信
本文详细介绍了如何使用Python通过CoppeliaSim远程API控制仿真机器人，实现外部通信的5个关键步骤。从环境配置到双向通信模式，再到实战案例和性能优化技巧，帮助开发者高效连接算法开发与物理验证。特别对比了B0与传统API的性能差异，提升工业自动化项目的开发效率。
【论文分享】【Agent】ProAgent: Building Proactive Cooperative Agents with Large Language Models
多智能体系统是由多个智能体组成的集合，这些智能体可以是软件程序、机器人、传感器或其他实体，它们各自具备一定的智能和自主性，并通过交互协作来实现单个智能体难以完成的复杂目标。
RocketMQ5.3.0-control-多 Master 多 Salve-异步复制-集群搭建
RocketMQ5.3.0-control-多 Master 多 Salve-异步复制-集群搭建（详细文档）
Latex | IEEE双栏排版中长公式的排版
IEEE双栏排版中，有时候公式过于长了，过宽，导致单栏排版不下。
基于three.js的室内全景3D展馆案例分享
3D场景展厅
在 SAP ABAP 里可靠地对接 FTP 服务器：从连接、命令到传输与排错的完整实战
本文详细介绍了在SAP ABAP中实现FTP文件交换的完整技术方案。文章首先解析了FTP协议的双连接特性（控制连接与数据连接），强调保持控制连接的重要性。随后详细说明了ABAP中FTP操作的四步标准流程：建立连接（包含HTTP_SCRAMBLE加密）、执行命令、断开连接和关闭RFC通道。文章提供了实战案例代码，演示如何将FTP文件复制到应用服务器再读取进内表处理，特别指出后台作业与前台操作的关键差异。最后总结了常见错误排查要点，包括连接模型、路径设置和资源释放等问题。全文通过SAP标准函数模块的调用顺序、典
OTG原理讲解
摘要：USB OTG（On-The-Go）是USB 2.0的重要功能，允许设备在主机和从机间动态切换，实现设备间的直接通信。其核心机制是通过ID引脚电平判断角色：Micro-USB中接地为Host，悬空为Device；USB-C则通过CC引脚协商。电路设计需处理ID引脚连接和VBUS电源控制，MCU根据ID状态配置USB控制器模式。典型应用包括手机连接U盘、键盘等外设，极大扩展了移动设备的连接能力。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
汉书HE8，好用的汇编代码编写工具
汉书HE8，好用的汇编代码编写工具早期编写汇编代码所用功能强大
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
ssm框架搭建
这个是一个在idea上成功跑起来的ssm框架项目，保存在这里方便以后使用直接下载
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
48
扫一扫
