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
2.6k累计阅读
2.6k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
Open-IM是由前微信技术专家打造的***开源***的即时通讯组件。Open-IM包括IM服务端和客户端SDK，实现了高性能、轻量级、易扩展等重要特性。开发者通过集成Open-IM组件，并私有化部署服务端，可以将即时通讯、实时网络能力快速集成到自身应用中，并确保业务数据的安全性和私密性。
（1） golang实现的跨平台的SDK：Open-IM-SDK-Core ，开发者不需要关心
（2）在Open-IM-SDK-Core 基础上生成的iOS版本SDK：Open-IM-SDK-iOS ，供开发者引用
（3）在Open-IM-SDK-Core 基础上生成的Android版本SDK：Open-IM-SDK-Android，供开发者引用
（4）在Open-IM-SDK
OpenIM离线docker部署及应用
介绍OpenIM：由前微信技术专家打造的基于 Go 实现的即时通讯（IM）开源项目，包括IM服务端和客户端SDK。开发者私有化部署，基于SDK二次开发，可以轻松替代第三方IM云服务，打造具备聊天、社交功能的app。无论是开发同城交友、企业办公亦或是当今最热门的元宇宙，还是在应用中集成IM功能，都非常便捷。OpenIM代码100%开源，开源协议Apache-2.0 License任何企业和个人都可以免费使用（包括商用）。请各位看官多多支持，转发和宣传，助力OpenIM成为开源IM的No1。
OpenIM私有化部署实战：Docker离线环境搭建与优化
本文详细介绍了OpenIM私有化部署在Docker离线环境中的实战经验，包括离线镜像准备、Docker Compose配置优化、部署验证及性能调优技巧。针对无网络环境下的特殊需求，提供了从硬件准备到安全加固的完整解决方案，帮助开发者在隔离网络中高效搭建稳定的即时通讯系统。
Open-IM源码部署
Open-IM源码部署
【项目实战】开源的即时通讯组件OpenIM入门介绍
IM，是即时通讯（Instant Messaging，简称 IM）OpenIM，是一个开源的、强大的即时通讯组件。OpenIM，提供了完整的即时通讯服务功能。OpenIM，被设计为高并发、高可用、易于扩展的架构。OpenIM，适合构建企业级的即时通讯平台。OpenIM，由一群热爱开源技术的开发者创建。OpenIM，旨在提供一个高性能、稳定、易用的即时通讯解决方案。OpenIM，是由IM技术专家打造的开源的即时通讯组件。
用Codex自动化挖掘电梯行业外贸客户：从关键词到22维深度分析
在B2B外贸客户开发中，精准定位垂直行业潜在客户是核心挑战。传统手动搜索效率低下，而数据驱动的工作流正成为解决方案。其原理在于将模糊的业务需求转化为机器可识别的结构化查询，通过组合行业核心关键词（如“Elevator Manufacturer”、“Elevator Maintenance”）与商业角色描述，系统性地从海量数据中筛选目标。这种自动化抓取技术的价值在于，它不仅生成客户名单，更通过多维数据分析（如公司规模、业务标签、数字足迹）构建初步的客户画像，为后续的优先级排序和个性化触达提供情报基础。在工业制
深度学习简介（框架）
深度学习是一种基于人工神经网络的机器学习方法，它通过模拟人脑的工作原理来解决复杂的数据模式识别问题。这种方法在图像识别、语音识别、自然语言处理等领域取得了显著的进展。深度学习是机器学习的一个子领域，它基于人工神经网络的学习算法，特别是那些具有多层（深层）结构的网络。这些深度神经网络能够学习数据的高级特征和模式。深度学习作为人工智能的关键驱动技术，已经在多个领域展现出其强大的能力。随着研究的深入和技术的发展，深度学习将继续推动人工智能的边界向前发展。
零基础玩转华为AP 4050DN-HD：FAT模式家庭无线网络配置实战
本文为零基础用户提供华为AP 4050DN-HD从FIT模式转换为FAT模式的完整实战教程。详细介绍了固件升级、模式转换以及配置家庭无线网络（包括路由、DHCP、Wi-Fi和NAT）的每一步操作，帮助用户将企业级瘦AP改造为高性能家庭胖AP，打造稳定可靠的家庭网络环境。
2023最新版kali安装教程
2023最新版kali安装教程 打开kali官网(https://www.kali.org/)，下载kali镜像
深信服PT1-AF实验考试
深信服PT1-AF实验考试实验练习可以去深信服的在线实验平台（链接如下）：深信服在线实验平台 (sangfor.com.cn)实验考试其实就是把所有的每一部分实验综合了起来以下内容仅做参考，不确保一样。
泛微Ecology9接口调用全流程：从配置到实战Java代码解析
本文详细解析了泛微Ecology9系统接口的完整调用流程，涵盖安全架构、服务端配置与客户端Java实战代码。重点阐述了基于RSA加密的认证机制、Token管理策略，并提供了企业级集成的核心代码示例与生产环境调优建议，帮助开发者安全、高效地实现系统对接。
让 RAP 弹窗参数真正变成必填：Excel Upload Action 的两种实现路径
摘要 在RAP应用中，action弹窗参数经常缺乏必填校验，导致用户体验差。本文提出两种解决方案：1)在action实现中手动校验，简单但交互不流畅；2)通过root abstract entity和abstract BDEF建模，利用field(mandatory)实现框架级校验，推荐此长期方案。后者将约束集中到模型层，使UI与执行期校验一致，特别适合ABAP Cloud环境。以Excel导入为例，强制填写备注和明确选择试运行，可避免审计问题，提升系统可靠性。
【SAP ME 3】JAVA 调用SAP PO WebService
Java调用SAP PO/PI WebService接口
WINCC7.3破解文件
WINCC7.3硬件够破解文件，有需要的下载！WINCC7.3硬件够破解文件，有需要的下载！
jre9下载 jdk9
(Java SE Runtime Environment 9.0) 9.0u175 官方最新版 【x86|x64】
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")
C# WinForm 基于DirectShow.dll编写的程序，可边预览边保存视频，音频和图片
基于微软的DirectShow.dll的二次开发1.自动识别连接的摄像头跟麦克风且可以对其进行选择（默认选中第一个）2.可录制wmv和avi两种格式的视频并且都带有音频，avi可以录制的分辨率和帧数进行选择，wmv默认进行了淸晰度优化如不喜欢可去除代码中有相关提示3.可在预览点开之后进行拍照，也可边录制视频边拍照4.添加了视频和音频的压缩功能（酌情使用）这次二次开发发现C#对DirectShow的相关信息太少，这次做完也不敢藏私共享出来，还望大家指正
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
3453
扫一扫
